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Choose an AI server for the work it must complete, not for a headline GPU count. Specify the model and serving stack, context and output lengths, concurrent agent sessions, latency and throughput targets, and where tools will run. Then size GPU memory and compute together with host CPU, system memory, storage, PCIe topology, and networking. Finally, benchmark the candidate using a representative agent workflow and record the full configuration; vendor specifications and results are useful inputs, not guarantees for every deployment.
Why agentic workloads change server sizing
An agent typically receives a goal and context, reasons with a model, calls a tool, incorporates the result, and repeats. That loop can keep sessions active while tools run and can grow the context as results and state accumulate. A server sized only for ordinary request volume may therefore miss the demands of long-lived sessions, concurrent work, orchestration, and data movement.
NVIDIA says a single agent task may involve 50–500 sequential model invocations and that agentic applications can generate up to 15 times more inference tokens. Those are NVIDIA-published descriptions of the workloads it discusses, not measured rates that apply to every agent or serving stack. NVIDIA also cites 30K–120K+ tokens as a context range for agentic inference. Treat these figures as planning prompts: measure the actual workflow you intend to deploy. NVIDIA agentic inference
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GPU memory must accommodate the model at the chosen precision or quantization as well as the key-value (KV) cache used to retain attention state for active sequences. Longer contexts and more simultaneous sessions can raise cache demand even when the model weights fit. NVIDIA estimates approximately 16–32 GB of KV cache for a 128K-token context with a 70B model; that is a vendor estimate for the stated model and context, not a universal cache formula. Actual needs depend on the model, implementation, precision, and serving configuration. NVIDIA agentic inference
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Agent systems also rely on CPU-side orchestration, retrieval, tool execution, sandboxing, and transfers between devices and storage. Decide whether those tasks share the inference host or have separate CPU capacity. If they compete with model serving, GPU specifications alone will not describe the system’s behavior.
Define the workload before choosing hardware
Write down a deployment profile before requesting quotes or comparing systems. Use the expected operating range and peak, not just an average request.
Rank #2
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
- Model and serving: model or models, precision or quantization, tokenizer, inference framework, and backend.
- Sequence shape: typical and maximum prompt, context, and output lengths; whether context grows across tool calls; and the cache state expected at serving time.
- Concurrency and service goals: typical and peak active sessions, target time to first token, per-token latency, throughput, and any completion-time objective.
- Agent execution: tool types, retrieval and data access, sandbox requirements, and whether tools run on the inference server or elsewhere.
- Deployment constraints: single server or cluster, data locality and privacy, power and cooling, rack space, networking, manageability, and software support.
These inputs distinguish a compact single-node design from a multi-GPU server or distributed deployment. They also make a benchmark meaningful: a result for short prompts and a warm cache does not establish performance for long contexts, cold starts, or sessions that pause for tools.
Choose a deployment size that fits the model and service target
There is no universal model-size cutoff that determines the right server. AMD’s guidance describes CPU-only systems as an option for small or medium models and some less latency-sensitive workloads; CPU-plus-GPU systems for larger models or faster response targets; and clusters for large-scale or complex multi-agent pipelines. AMD includes expert agents among workloads mapped to CPU and CPU-plus-GPU options, while emphasizing that capacity depends on workload, model, memory, software, orchestration, and configuration. Use this as vendor guidance rather than an industry-wide dividing line. AMD EPYC 9005 inference guidance
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| Deployment approach | When to consider it | What to validate |
|---|---|---|
| CPU-only | Small or medium models, or workloads where latency is less demanding, as described in AMD’s guidance. | That the chosen model, context, concurrent sessions, and response target are acceptable on the actual CPU system. |
| CPU plus GPU | Larger models or workloads with faster response targets, according to AMD’s guidance. | GPU memory and compute, KV-cache headroom, CPU capacity for orchestration and tools, and how devices connect to the host. |
| GPU cluster | Large-scale inference or complex multi-agent pipelines, in AMD’s guidance. | Distributed-serving support, network behavior, workload placement, scaling, and the cost and operational complexity of multiple nodes. |
Size GPU memory, host memory, and system topology
Estimate memory for weights and active sessions
Start with the selected model and its deployed precision, then account for the memory used by active sequences and the serving implementation. Context length and simultaneous sessions are key inputs because both can increase KV-cache pressure. Check the serving software’s own memory behavior and test the intended concurrency rather than assuming that a model that loads successfully will meet the target under load.
NVIDIA describes a tiered context-memory path using GPU HBM, host DRAM, and NVMe in its Dynamo design. This is an example of an architecture, not a requirement to use that product or a substitute for validating latency and capacity on the selected system. NVIDIA agentic inference
Rank #4
- EVOLUTION CORE ULTRA 9 285H MINI PC - GMKtec EVO-T1 is the next evolution in AI mini PC Ultra 9 series. The Core Ultra 9 285H offers 16 cores (six P-cores + eight E-cores + two LPE-cores) and 16 threads with a turbo clock of 5.4 GHz. It is currently one of the best value for performance AI mini PC computers.
- AI NPU - The 285H features an Intel AI Boost NPU, capable of up to 13 TOPS (Tera Operations per Second) for INT8 calculations, which is designed to accelerate AI tasks.
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- 64GB DDR5 RAM + 1TB SSD - The EVO-T1 is equipped with Dual 32GB (Total 64GB) SO-DIMM DDR5 5600MHz memory sticks. 2TB PCIE 4.0 SSD Drive with 3x M.2 2280 Expansion slots. Each slot capable of reading up to 4TB. (12TB MAX)
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Check CPU, system RAM, PCIe, and local storage
For NVIDIA-certified inference-server profiles, the current configuration guide recommends PCIe Gen 5-capable CPUs, at least two CPU sockets, at least six physical CPU cores per GPU, and system memory at least twice the total GPU memory. It also recommends balancing GPUs across CPU sockets and PCIe root ports. The guide calls for one local NVMe drive per CPU socket and at least 1 TB of local storage, and discusses GPU-specific PCIe needs and placement of NICs and NVMe devices relative to PCIe switches and root complexes. These are recommendations for the guide’s certified profiles; check the selected GPU and system documentation rather than treating them as universal minimums. NVIDIA-Certified Systems Configuration Guide
For multi-node inference, that same guide recommends at least 200 Gbps networking. Apply this as NVIDIA guidance for the covered profiles, then verify the network needs of the actual distributed serving stack and workload. Edge deployments may have fewer CPU cores, less system memory, fewer GPUs, and lower bandwidth requirements than data-center systems, but still need suitable low-latency networking and storage. NVIDIA’s discussion of this trade-off dates from 2022, so confirm current hardware and software support before buying. NVIDIA server selection article
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Compare candidate servers on useful outcomes
Compare systems against the same workload profile, not just GPU model or peak tokens per second. Evaluate these dimensions together:
- Model and memory fit: model size and precision, GPU memory, context limit, and cache footprint at target concurrency.
- Latency and throughput: time to first token, inter-token latency, aggregate token throughput, and sessions or tasks served at realistic concurrency.
- Agent execution: CPU capacity for orchestration, retrieval, sandboxed tools, and data movement, including whether those tasks contend with serving.
- Scale and topology: single node versus distributed serving, CPU-to-GPU balance, PCIe lanes and root complexes, and inter-node network.
- Operations: framework support, manageability, security, power, cooling, rack space, and local-data requirements.
- Economics: acquisition and operating cost per useful completed task, rather than peak throughput alone. The cited sources do not establish a neutral total-cost comparison, so calculate this for the intended deployment.
Vendor benchmark results can help identify candidate configurations, but they are not independent cross-vendor conclusions. For example, AMD’s published host-CPU comparisons identify a particular dual-socket EPYC 9575F and Intel Xeon system with eight NVIDIA B200 GPUs, along with software, memory, and BIOS details; AMD states the testing was as of 2025-10-24. Do not generalize a result from that configuration to other systems. AMD EPYC 9005 host CPU results
Benchmark with a representative agent workflow
Run the intended model and backend with representative prompt and output distributions, target context, realistic concurrency, and actual tool pauses. Include the cache conditions that matter in production. Measure latency and throughput under load, but also track resource use and whether tasks complete successfully; a fast isolated generation is not evidence that an end-to-end agent workflow meets its service goal.
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Record enough detail for someone else to reproduce or interpret the result. NVIDIA’s inference reference architecture lists model, tokenizer, backend, container image, hardware profile, network mode, storage path, prompt and output profiles, concurrency, cache state, and software versions as benchmark provenance. AMD’s EPYC/Nutanix user guide describes varying input and output token lengths and simulated parallel users, then measuring per-token latency, time to first token, throughput, and CPU and memory utilization. These are practical measurement templates, not single benchmarks that represent every agent workflow. NVIDIA inference reference architecture · AMD EPYC/Nutanix user guide
Quick Recap
- Use the same model, tokenizer, serving software, and relevant software versions when comparing candidates.
- Test short and long sequences, typical and peak concurrency, and the cache state expected in service.
- Include tool waits and the CPU-side work that accompanies inference; measure the complete task as well as model generation.
- Capture the exact hardware, memory, firmware or BIOS settings, network, storage path, and benchmark settings alongside results.
- Repeat the workload on the candidate platform before treating a vendor result as a procurement estimate.
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