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Choose the network only after you know where the model fits. If one GPU or one server can hold the model and its KV cache at your target concurrency, keep inference on that machine. If several GPUs in one server are needed, use intra-node tensor parallelism. When the model exceeds one server, keep tensor-parallel groups inside each server and connect servers with pipeline parallelism when possible. Use ordinary private Ethernet for low-rate control traffic or experimental llama.cpp RPC, but use an InfiniBand or RoCE fabric with RDMA for workloads that move tensors continuously between nodes. There is no universal “10GbE is enough” answer: usable speed depends on model, quantization, context length, concurrency, GPU topology, software and measured latency.
What network setup should I choose for distributed local AI inference?
Start with the smallest topology that satisfies model capacity and throughput. A network should solve a capacity problem, not create a distributed system by default.
| Situation | Recommended setup | Why it fits | Main trade-off |
|---|---|---|---|
| The model and KV cache fit on one GPU | No distributed network | All GPU memory traffic stays local, with the fewest failure points. | You cannot add capacity without moving to a larger GPU or server. |
| The model fits in one server with multiple GPUs | Intra-node tensor parallelism over the server’s GPU interconnect | GPU-to-GPU links inside one chassis are normally faster and more predictable than a network. | The server must have enough GPU memory, compatible interconnects and power. |
| llama.cpp workers on ordinary local machines | Private wired Ethernet using TCP, with the local RPC cache enabled | TCP is the baseline transport and caching prevents repeated transfer of large tensors. | The RPC backend is experimental and insecure if exposed to untrusted networks. |
| Cross-node tensor parallelism | InfiniBand or RoCE with matching RDMA-capable NICs and switch | Tensor parallelism creates continuous cross-node GPU communication, so latency, bandwidth and RDMA matter. | Higher hardware cost and more demanding configuration. |
| Pipeline stages on separate servers | High-speed Ethernet can be adequate if measured latency and throughput meet the workload | Only stage boundaries communicate for each token step, reducing continuous all-to-all traffic. | Pipeline bubbles, stage imbalance and link failures still reduce throughput. |
The practical design rule is to keep tensor-parallel traffic inside a host whenever possible and use pipeline parallelism between hosts when the network cannot sustain constant tensor exchange. This follows the topology guidance documented for vLLM and NVIDIA multi-node deployments.
How does model fit determine whether you need a network?
Measure memory before buying switches or adapters. The model weights are only the first requirement; runtime buffers, activations and the KV cache grow with context length and concurrent requests.
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Measure the three capacity limits
- Weights: Calculate the resident size of the selected precision or quantization, then leave headroom for runtime allocations.
- KV cache: Estimate memory at the longest context and target simultaneous requests. A model that loads at batch one may fail when several long conversations arrive.
- Throughput target: Record prompts per second, generated tokens per second and acceptable first-token latency. A capacity-only deployment may need fewer nodes than a throughput deployment.
If all three fit on one GPU with acceptable latency, distributing the model adds communication and operational risk without solving a reader-visible problem. If the model fits in one host, use that host before splitting across PCs.
When a second host is justified
Add another server when the required model plus cache cannot fit in the available GPUs, or when one server cannot deliver the required concurrent throughput. Document the exact model revision, quantization, context limit and concurrency target; these variables determine whether a proposed link is sufficient.
What is the difference between tensor parallelism and pipeline parallelism?
Tensor parallelism (TP) splits individual layer computations across GPUs, while pipeline parallelism (PP) assigns groups of layers to different GPUs or hosts. TP needs frequent synchronization; PP sends activations between stage boundaries.
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Tensor parallelism
TP divides matrix operations so several GPUs participate in nearly every layer. Within one server, this can use the platform’s high-speed GPU interconnect. Across servers, every decoding step can exchange substantial data, making bandwidth, latency and RDMA availability decisive. NVIDIA states that multi-node TP requires continuous cross-node GPU communication and that performance depends on network bandwidth, latency and RDMA.
Pipeline parallelism
PP places consecutive layer ranges on separate devices. Each token’s activations cross the network between stages rather than every layer operation synchronizing across all nodes. PP therefore tolerates a slower inter-node link better than cross-node TP, but it introduces pipeline bubbles and can be limited by the slowest stage.
Combining TP and PP
A common multi-node arrangement sets tensor-parallel size to the number of GPUs per node and pipeline-parallel size to the number of nodes. For example, two servers with eight GPUs each can use TP=8 and PP=2, provided the software, model and memory fit that layout. The exact values are workload- and version-dependent; validate them on the intended hardware.
| Characteristic | Tensor parallelism | Pipeline parallelism |
|---|---|---|
| What is split | Layer operations and tensors | Contiguous groups of layers |
| Inter-node communication | Continuous and communication-heavy | Activation transfers at stage boundaries |
| Network sensitivity | Very high; latency, bandwidth and RDMA all matter | Lower than TP, but still sensitive to link reliability and stage balance |
| Typical placement | Keep the TP group within one host when possible | Use PP between hosts when the model exceeds one host |
| Primary risk | Network stalls can affect every layer step | Pipeline bubbles or an imbalanced stage can leave GPUs idle |
Is 10GbE enough for multi-GPU inference?
Sometimes, but no published general speed threshold establishes that 10GbE is sufficient for all multi-GPU inference. It may work for control traffic, llama.cpp RPC experiments, or carefully measured pipeline-parallel stages; it is a poor default for cross-node TP with high token concurrency.
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Cases where 10GbE may be acceptable
- The model is partitioned with pipeline parallelism and each stage sends relatively small activation messages.
- Concurrency is low and latency targets are modest.
- llama.cpp RPC is used for a private, non-critical workload and the local cache eliminates repeated movement of large tensors.
- Benchmarking on the exact model, context and request mix shows that network wait is not the bottleneck.
Cases where 10GbE is a warning sign
- Cross-node TP synchronizes large tensors on every layer step.
- Long contexts or high concurrency increase activation and cache traffic.
- Several jobs share the same uplink or switch, creating queueing and jitter.
- The GPUs are fast enough that a network stall dominates token latency.
Do not infer adequacy from link-rate marketing. Check sustained application throughput, tail latency and GPU idle time while the intended workload runs. A nominal 10GbE connection can underperform because of oversubscribed switches, CPU copies, PCIe limitations, interrupt handling or mismatched MTU settings.
When should I use InfiniBand or RoCE with RDMA?
Use an RDMA fabric when cross-node TP or another NCCL-heavy workload moves tensors continuously and latency consistency matters. InfiniBand and RoCE are the preferred high-speed paths in the documented multi-node guidance.
InfiniBand
InfiniBand is a purpose-built fabric with established GPU-cluster tooling. Choose it when you need predictable low latency, high throughput and a switch/NIC ecosystem designed for collective communication. Verify that every server has compatible adapters, optics or cables, switch ports and driver support.
RoCE
RoCE carries RDMA over Ethernet. It can reuse Ethernet cabling and switching practices, but lossless or near-lossless behavior requires careful configuration of priority flow control, congestion management, MTU and routing. A RoCE NIC connected to an ordinary unmanaged switch is not automatically an effective RDMA cluster.
Why raw TCP is not an equivalent substitute
NCCL documentation reports raw TCP sockets as inefficient for high-performance collective communication compared with RDMA paths. TCP remains useful for control planes and software that explicitly supports it, but it should not be assumed to deliver TP performance merely because the link has a high advertised rate.
How do I connect two PCs with llama.cpp RPC safely?
Use a private wired network, keep both machines on the same compatible build, and treat RPC as experimental infrastructure. The llama.cpp maintainers describe the RPC backend as proof-of-concept, fragile and insecure; never bind it to an open or untrusted network.
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- Prepare both hosts. Build llama.cpp with RPC support on the worker and the main host. Keep compiler, CUDA or other accelerator runtimes, driver versions and model formats aligned.
- Place the hosts on an isolated segment. Use a dedicated switch or VLAN with private addresses. Do not publish the RPC listener through a router, port-forward, public load balancer or VPN exit that admits unknown clients.
- Start the worker listener. Bind the worker to its private interface and the port selected for your build. Allow inbound traffic only from the main host’s private address.
- Start the main process with worker endpoints. Supply the worker addresses with the build’s
--rpcoption. For example, an internal deployment might reference10.42.0.12:50052and10.42.0.13:50052; confirm the supported port and syntax in the version you installed. - Enable the RPC local cache. The cache keeps large tensors on the worker so they are not transferred for every request. Size and location depend on the llama.cpp version and available local storage.
- Run a small validation request. Confirm that both workers report activity, that the model loads without repeated transfers and that token latency remains stable over several requests.
- Lock down the segment. Apply host firewall rules allowing only the main host and required port. Remove the listener when the cluster is not in use.
RPC transport support is version-sensitive. Some builds can negotiate RDMA when both peers support it, but TCP is the baseline; do not assume RDMA is active without checking runtime output.
How should I configure vLLM or an NCCL-based multi-node stack?
Use identical software environments, stage the model on every node or expose dependable shared storage, then configure the process groups to match physical topology.
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- Use the same operating-system image, accelerator driver, CUDA or ROCm runtime, framework version and serving build on every node.
- Use the same model files, tokenizer and configuration. A checksum comparison catches silent model drift.
- Confirm that every GPU is visible in the expected order and that the host-to-GPU PCIe topology is comparable.
Choose process sizes from hardware
For two eight-GPU nodes, a documented pattern is TP=8 and PP=2: each node owns an eight-GPU TP group, and the two groups form the pipeline. In a vLLM deployment, the corresponding options are commonly expressed as --tensor-parallel-size 8 and --pipeline-parallel-size 2; option names and support can change by release, so check the installed version.
Verify the transport
Enable NCCL diagnostics for a test run. A log containing NET/IB/GDRDMA indicates an RDMA path, while NET/Socket indicates raw TCP. If the process silently falls back to sockets, investigate interface selection, driver compatibility, firewall rules, RDMA configuration and switch settings before judging model performance.
What physical network hardware and cabling must match?
A fast switch alone does not create a usable distributed-inference fabric. The NIC, transceiver or cable, switch port, driver and software collective library must support the same transport.
| Design item | What to verify | Typical failure if ignored |
|---|---|---|
| NICs | Speed, RDMA mode, PCIe generation, firmware and driver support | Link negotiates but the application falls back to sockets or saturates PCIe. |
| Switch | Port speed, buffering, congestion controls, RoCE features or InfiniBand fabric management | Packet loss, pauses and tail-latency spikes under collective traffic. |
| Cables and optics | Correct connector, transceiver class and supported distance | Link flaps, reduced rate or no link at all. |
| Port count | Enough ports for every node and any redundant or storage links | Shared uplinks become an unexpected bottleneck. |
| GPU topology | Peer access, NVLink or equivalent interconnect, PCIe root complexes | One GPU pair becomes the slow path and limits the whole group. |
| Software support | Matching NCCL, drivers, framework and transport libraries | Initialization errors or an unannounced TCP fallback. |
Distance also matters. Short direct-attach cables can be inexpensive inside one rack; longer runs may require approved optics and additional switch capacity. The correct choice depends on the room layout and supported cable length, not only the advertised port rate.
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Pre-stage the model on every node or use storage that all nodes can read reliably. Loading weights over a slow shared path at startup can delay recovery, while serving active tensor traffic from the same congested storage network can add jitter.
Local model copies
Local NVMe or other directly attached storage gives predictable startup and avoids making the file server a runtime dependency. Keep enough free space for the model, tokenizer, temporary conversion files and the llama.cpp RPC cache where applicable.
Shared storage
Shared storage simplifies updates, but measure metadata and read throughput during simultaneous node starts. Use versioned directories and checksums so every process sees identical files. Separate storage traffic from the inference fabric when the switch or NICs would otherwise be oversubscribed.
How do I secure a distributed inference network?
Treat every inference link as a private service network, not as an internet-facing API.
- Use a dedicated switch or VLAN with non-routable addresses.
- Permit only the scheduler, main host and known workers to reach RPC or collective-communication ports.
- Bind listeners to the private interface rather than all interfaces.
- Block inbound access from guest Wi-Fi, office networks and public interfaces.
- Keep host firewalls enabled and document the minimum port rules for the selected software release.
- Monitor connection attempts and remove unused listeners after testing.
- Place a separate authenticated API gateway in front of inference if users outside the private segment need access; do not expose the worker RPC protocol directly.
This isolation is especially important for llama.cpp RPC because its maintainers explicitly warn that the backend is insecure by default.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I benchmark the network before buying hardware?
Benchmark the complete model-serving path, not just a synthetic link test. Use the production quantization, context lengths, request concurrency and batching policy.
- Establish a single-host baseline. Record prompt processing rate, generation rate, first-token latency, tokens-per-second at steady state and GPU utilization.
- Test the proposed topology. Run the same workload with the intended TP, PP or RPC arrangement and identical software versions.
- Capture tail latency. Measure p50, p95 and p99 first-token and per-token latency while several requests run concurrently.
- Inspect GPU idle periods. Long synchronized gaps indicate communication or synchronization stalls even when average utilization looks acceptable.
- Read transport logs. Confirm whether NCCL uses
NET/IB/GDRDMAor falls back toNET/Socket. For llama.cpp, verify cache hits and worker activity. - Repeat during contention. Add storage traffic and the expected number of simultaneous requests. A design that works only on an idle switch is not production-ready.
- Test failure recovery. Disconnect a worker or restart a process in a controlled window and document restart time, partial-failure behavior and required operator steps.
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What are the cost and operational trade-offs?
Hardware cost is only one part of distributed inference. Every additional node adds power, cooling, firmware, monitoring and failure domains.
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|---|---|---|---|
| Single GPU | Lowest | Lowest | Models that fit with the required cache and latency. |
| One multi-GPU host | High GPU, chassis and power requirements | Moderate; no inter-host fabric | Models needing several GPUs but not multiple servers. |
| Multiple hosts over Ethernet | Additional NICs and switch ports | Moderate to high; easier cabling but still requires isolation and testing | Pipeline stages, control traffic and experimental RPC. |
| Multiple hosts over InfiniBand or RoCE | Specialized NICs, switch and optics or cables | High; requires RDMA, driver and congestion-management expertise | High-throughput cross-node TP and NCCL-heavy serving. |
Choose the least complex option that meets the measured target. Spending on RDMA will not compensate for an undersized GPU pool, an imbalanced pipeline or a model that is limited by KV-cache capacity.
What should I check when distributed inference is slow or unstable?
The job falls back to TCP
Check NCCL logs for interface selection and transport. Verify that the RDMA device is visible, drivers and firmware match, the correct NIC is selected and firewalls are not blocking required traffic. Confirm switch configuration before changing model settings.
Workers repeatedly reload tensors
For llama.cpp, confirm that the local RPC cache is enabled, writable and large enough. Check that workers use the expected model and that cache files are not deleted between requests.
One GPU is idle while others work
Inspect GPU topology, TP grouping and pipeline-stage assignment. A slow PCIe path, uneven layer placement or a single overloaded stage can hold back the entire job.
Latency rises only under concurrency
Measure switch queueing, NIC utilization, CPU copy time and KV-cache pressure. Reduce concurrency temporarily to determine whether the limit is network capacity, memory or scheduler contention.
A worker cannot join
Compare software versions, model checksums, hostnames or private addresses, clock configuration and firewall rules. Then run a minimal two-node test before restoring the full model and concurrency.
Which network design fits common two-host scenarios?
Two ordinary workstations running llama.cpp
Use a private wired Ethernet switch, TCP RPC and the local RPC cache. Keep the listener reachable only from the trusted main host, and regard the arrangement as experimental rather than a hardened service platform.
Two eight-GPU servers serving a large model
Keep eight-way TP inside each server and use two-way PP across the servers when the model and software support that layout. Prefer InfiniBand or RoCE with RDMA, confirm NCCL selects the RDMA path, and pre-stage identical model files.
Two servers with a fast Ethernet link but no RDMA
Favor PP over cross-node TP if the measured activation traffic meets latency and throughput targets. If only TP can satisfy memory or quality requirements, expect communication overhead and benchmark before committing to production.
What is the final selection checklist?
- Model weights, KV-cache demand, context length and target concurrency are documented.
- A single GPU and a single-host option were tested before adding nodes.
- TP is kept within hosts unless cross-node traffic was measured and justified.
- PP stage balance and pipeline bubbles were checked.
- NICs, switches, cables, ports, drivers and GPU topology are compatible.
- Model files and caches are local or on storage proven adequate under concurrent starts.
- Private VLAN or switch isolation and host firewall rules are in place.
- NCCL logs confirm the intended transport instead of an accidental socket fallback.
- The exact model, quantization, context and concurrency were benchmarked on the final hardware.
Frequently Asked Questions
Can Wi-Fi connect two machines for local AI inference?
It may work for setup, file transfer or a very low-rate control path, but variable latency, interference and roaming make Wi-Fi unsuitable for predictable tensor or pipeline traffic. Use wired links for the inference path and keep wireless access on a separate management network.
Does RDMA reduce GPU memory usage?
RDMA changes how data moves between hosts; it does not reduce the model weights or KV-cache capacity required by the GPUs. You still need enough aggregate accelerator memory for the selected partitioning strategy.
Should I use one large server or several smaller PCs?
One server is usually simpler when the model fits because it avoids inter-host links, synchronization and independent failure domains. Several PCs become attractive when available GPU memory, power limits, physical availability or incremental expansion outweigh that operational simplicity.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHow often should I retest a distributed inference network?
Retest after changing the model, quantization, driver, serving framework, NIC firmware, switch configuration or target concurrency. Transport selection and performance can change after software upgrades even when the cabling is unchanged.
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