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The right cloud GPU is the least expensive configuration that can hold your exact model and serving workload with usable memory left for runtime overhead and KV cache, then meets your latency, throughput, availability and reliability targets. Start by fixing the model, precision or quantization, context window, serving runtime and concurrent users. Check usable GPU memory before looking at vendor tier or hourly price; then compare GPU topology, host resources, storage, networking, regional capacity and measured cost per token.
Why model size alone is not enough
“What GPU setup do I need to host this model?” has no reliable answer until “this model” includes its serving configuration. Two deployments with the same parameter count can have materially different memory and performance requirements. Precision, quantization, context length, runtime, batch behavior and concurrency all change the amount of memory and compute the service needs.
NVIDIA’s current vGPU deployment guidance describes its memory figures as rough estimates. It specifically notes that actual use varies with the model, quantization, context length and KV cache. Treat every sizing number as a starting point for a test, not as a guarantee that a machine will load the model.
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- Model and revision: record the exact open-weight model, checkpoint revision and tokenizer. A parameter label such as 8B or 70B is not a complete deployment specification.
- Precision or quantization: write down FP16, BF16, FP8 or the particular quantized format. Lower precision can reduce weight memory, but the runtime and quality target must support it.
- Context window: specify the maximum prompt-plus-generation length you will permit. Longer context increases KV-cache use, often enough to change the required GPU class.
- Serving runtime: identify the engine and its version, including whether it uses tensor or pipeline parallelism, continuous batching, CUDA graphs or other memory features.
- Concurrency and traffic shape: estimate simultaneous requests, requests per second, input and output token lengths, and whether traffic is steady or bursty. A single interactive user and a batch API have different bottlenecks.
Put these values in a short deployment worksheet before requesting quotes. If any value is unknown, use a conservative upper bound for the first capacity check and mark it for measurement during a pilot.
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Size usable GPU memory first
GPU memory is the feasibility gate: weights, runtime allocations, activations and the KV cache must fit at the same time. Advertised capacity is not identical to memory available to your process. In a vGPU profile, software reserves part of the frame buffer, so the VM sees less than the configured profile size. Leave headroom rather than targeting a model’s estimate exactly.
Published sizing examples
The following figures come from NVIDIA documentation accessed in 2026. They are approximate or profile-specific, not universal minimums.
| Model and configuration | Published GPU-memory figure | How to use it |
|---|---|---|
| 7B–8B at FP16/BF16 | Approximately 16 GB (NVIDIA vGPU guide) | Initial weight-capacity estimate; add runtime and KV-cache headroom. |
| 7B–8B at FP8 | Approximately 10 GB (NVIDIA vGPU guide) | Useful only when the selected runtime and quality target support FP8. |
| 13B at FP16/BF16 | Approximately 28 GB (NVIDIA vGPU guide) | A 24 GB profile is unlikely to be a safe starting point; validate with the full context and runtime. |
| 70B at FP8 | Approximately 70 GB (NVIDIA vGPU guide) | An 80 GB card may still lack practical headroom after reservations and cache; benchmark the actual service. |
| Llama 3.1 8B at FP16 | 24 GB (NVIDIA NIM 1.6.0 supported-model table) | This profile-specific figure is higher than the rough vGPU estimate because the supported runtime configuration has its own requirements. |
| Llama 3.1 405B at FP16 | 240 GB (NVIDIA NIM 1.6.0 supported-model table) | A single 80 GB GPU cannot satisfy this listed requirement; evaluate a supported multi-GPU design. |
The difference between the two 8B figures is not an error to average away. The vGPU guide offers a rough model estimate, while the NIM table describes a particular supported-model configuration. Runtime overhead, reserved memory and context assumptions explain why a production profile can need more than the raw weight estimate.
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Do not fill the card with weights and assume the rest will fit. KV-cache allocation grows with the number of active sequences and their context lengths. Runtime workspaces, communication buffers and framework reservations also consume memory. A deployment that loads successfully at one short prompt may fail or start evicting work at the advertised maximum context or at higher concurrency.
Use the published number to reject clearly undersized options, then run the intended runtime with representative prompts. Record free memory at startup, during the longest context, and at peak concurrency. If the test reaches an out-of-memory condition, reduce context or concurrency only if that is acceptable to your product; otherwise move to a larger or multi-GPU profile.
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Map the requirement to real cloud machine types
Once the usable-memory check passes, compare complete instance shapes rather than isolated GPU names. Google Cloud’s Compute Engine tables illustrate the range: A2 and A3 families offer one- through multi-GPU types, with different host memory, local storage and networking characteristics. Availability and provisioning are separate questions from what the table lists.
| Example machine type | GPU configuration | Published aggregate GPU memory | Implication |
|---|---|---|---|
a2-highgpu-1g (A2 Standard) |
1 A100 | 40 GB | Suitable only when the model and runtime fit within usable capacity and the host shape meets CPU, RAM and I/O needs. |
a2-ultragpu-1g (A2 Ultra) |
1 A100 | 80 GB | More memory for larger single-GPU profiles; nominal 80 GB is not the same as 80 GB available to the VM. |
a3-highgpu-1g (A3 High) |
1 H100 | 80 GB | Check the runtime’s H100 support, local capacity and price before treating it as a drop-in replacement for an A100. |
| A3 High 8-GPU type | 8 H100 GPUs | 640 GB aggregate | Aggregate memory enables larger or replicated workloads, but only if the model-parallel runtime and GPU interconnect deliver acceptable performance. |
Aggregate memory does not automatically behave like one large, fast memory pool. A sharded model must move activations or parameters between GPUs. Interconnect bandwidth, topology, host configuration and the runtime’s parallelism strategy determine whether an eight-GPU shape is efficient or merely expensive.
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Compare the instance on six axes
| Axis | What to compare | Why it changes the decision |
|---|---|---|
| GPU memory | Usable memory after reservations, plus headroom for runtime and KV cache | Determines whether the service loads and remains stable at its target context and concurrency. |
| Model configuration | Exact checkpoint, precision or quantization, context window and runtime | Memory estimates vary with every one of these inputs. |
| GPU count and topology | Single versus multi-GPU, interconnect, placement and host configuration | Large profiles may require sharding; poor communication can erase the benefit of extra GPUs. |
| Throughput and latency | Time to first token, per-token latency, requests per second and output rate at target concurrency | Hardware labels do not predict your application’s response time. NVIDIA’s supported profiles distinguish latency and throughput configurations for some models. |
| Total cost | GPU-hour, host charges, storage, network egress, idle time and utilization; preferably cost per token | A low hourly rate can lose when the machine is underutilized or produces fewer tokens per second. |
| Capacity and geography | Region, quota, reservation options and current stock | A listed machine type may not be provisionable to your account when you need it. |
Understand hourly prices and cost per token
Hourly price is useful for a first estimate, but it is not a performance comparison. Hugging Face’s endpoint pricing page, accessed in 2026, listed these examples:
| Provider and GPU entry | Listed price | Qualification |
|---|---|---|
| AWS L40S ×1 | $1.80 per hour | Access-time endpoint listing; not a complete instance-cost comparison. |
| AWS A100 ×1 | $2.50 per hour | Access-time endpoint listing; quota and availability can apply. |
| GCP A100 ×1 | $3.60 per hour | Access-time endpoint listing; compare the full machine shape and region. |
| GCP H100 ×1 | $10 per hour | Access-time endpoint listing; higher hourly cost may be justified by measured throughput. |
These are snapshots, not recurring universal rates. Confirm the current regional price, attached CPU and RAM charges, persistent-disk or local-SSD costs, network egress, taxes and any reservation commitment. Include startup and shutdown time in the bill if you scale machines for bursts.
Use cost per useful output
NVIDIA’s H100 page relays SemiAnalysis InferenceX results dated April 2026: for GPT-OSS-120B, a B200 was reported at approximately $0.02 per million tokens at 55 transactions per second per user with TensorRT-LLM, while an H100 was reported at approximately $0.09 per million tokens at 66 transactions per second per user with vLLM. This is a vendor-hosted report of different GPUs and runtimes, not a like-for-like ranking. It demonstrates why a cost-per-token figure must carry its workload, runtime, throughput target and test date with it.
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For your pilot, calculate:
- Measure completed input and output tokens during a representative interval.
- Divide the fully loaded instance cost for that interval by the completed token count.
- Repeat at your planned concurrency and at a stress level that exposes queueing.
- Include idle periods, failed requests, retries and any second machine needed for high availability.
Report latency separately. A configuration can produce cheap tokens while violating your interactive response-time objective, or meet latency by leaving expensive GPUs mostly idle.
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Capacity is a deployment requirement, not an operational detail to solve later. Google Cloud notes capacity-reservation or other provisioning options for some newer machine types. Hugging Face notes that quota may be required for some endpoint types. Check the exact region, account quota, reservation process, lead time and outage behavior before committing your architecture.
- Region: choose a location that satisfies data-residency and user-latency requirements, then verify that the desired GPU is offered there.
- Quota: confirm GPU and instance quotas for the account and project, including limits for multiple machines if you plan to scale out.
- Reservation: determine whether a reservation or capacity commitment is needed for predictable access to newer GPU families.
- Fallback: define a second region or an alternate GPU profile with a tested model configuration; do not assume another family can run the same shard layout without retesting.
A repeatable selection procedure
- Write the workload contract. Record model revision, precision, maximum context, expected concurrent sequences, input and output lengths, runtime, target time to first token and target output rate.
- Set the memory floor. Use the closest published estimate as a screening number. Add explicit headroom for KV cache, runtime workspaces, vGPU reservation and growth in concurrency.
- List feasible shapes. For each provider and region, record GPU model, usable memory, GPU count, host RAM, local SSD, network bandwidth, hourly price and provisioning status.
- Decide whether to shard. Prefer a single GPU when it comfortably fits and meets performance goals. Move to multi-GPU only when capacity, throughput or availability requires it, and confirm that the runtime supports the selected topology.
- Build a cost model. Add compute, storage, egress, startup time, idle utilization and redundancy. Convert the result to cost per million tokens at the measured workload.
- Run a representative benchmark. Use the production checkpoint and runtime, the real prompt and completion-length distribution, and several concurrency levels. Capture memory, latency percentiles, throughput, errors and power or thermal throttling if exposed by the provider.
- Verify capacity. Obtain quota approval or a reservation in the target region and confirm that the exact instance shape can be created before finalizing launch dates.
- Document a fallback. Record the tested context limit, quantization, shard count and autoscaling behavior for the alternate profile so an incident does not become an unplanned redesign.
Worked sizing decisions
7B–8B interactive assistant
For FP16/BF16, NVIDIA’s rough estimate is about 16 GB; its Llama 3.1 8B FP16 NIM profile lists 24 GB. Start by testing a 40 GB or 80 GB single-GPU shape only after checking the runtime’s actual profile and required context. If FP8 is supported and acceptable for quality, the rough estimate is about 10 GB, but the saved weight memory does not remove KV-cache or runtime allocations. Choose the smaller card only when measured headroom remains at peak concurrency.
13B at FP16/BF16
The vGPU guide’s approximate figure is 28 GB. A 40 GB A100 shape may be a candidate, but usable capacity, context and vGPU reservations determine whether it is safe. Test the longest permitted prompt rather than only a short smoke test. If the service needs more cache or higher concurrency, an 80 GB profile may be cheaper than adding operational complexity to a marginal 40 GB deployment.
70B at FP8
NVIDIA cites approximately 70 GB. An 80 GB A100 or H100 has the nominal capacity, yet the margin can disappear through reservations, runtime workspaces and cache. If the measured configuration cannot maintain its context and concurrency targets, options include a larger usable-memory profile, a supported quantization, or a multi-GPU layout. Do not infer that any eight-GPU machine will be faster: test the interconnect and sharding plan.
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Llama 3.1 405B at FP16
NVIDIA’s NIM 1.6.0 table lists 240 GB. That immediately rules out a single 40 GB or 80 GB card for this profile. Evaluate a supported multi-GPU instance with enough usable aggregate memory, then benchmark communication overhead, startup time, failure recovery and cost per token. The table’s number describes the listed NIM configuration; it is not a promise that every runtime or context length will fit in exactly 240 GB.
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Host RAM and CPU
Host RAM holds model files, tokenizer data, staging buffers and application processes. CPU capacity affects request handling, tokenization, batching and data movement. A GPU that fits the weights can still sit idle if the host cannot feed it or if the web service and runtime compete for memory.
Local SSD and startup behavior
Large checkpoints take time to download and load. Compare local SSD size and throughput with the checkpoint and cache layout you will use. Measure cold-start time separately from warm-request latency if you plan scale-to-zero or frequent replacement. Keep enough disk for the model, temporary conversion files, logs and the runtime’s cache without silently filling the boot volume.
Network path
Network bandwidth and egress affect both multi-GPU deployments and user-facing latency. For sharded inference, the internal GPU interconnect and host topology are usually more important than internet bandwidth, while remote clients care about region, routing and egress charges. Test the complete path from your application to the inference endpoint, not just the GPU kernel time.
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- Use production artifacts: the same checkpoint revision, tokenizer, quantization and serving runtime you will deploy.
- Use a workload matrix: short and long prompts, short and long completions, and the expected concurrency range.
- Capture percentiles: report median and tail time to first token and inter-token latency, not only an average.
- Watch memory over time: include cache growth, batching and a sequence that reaches the maximum context.
- Test failure behavior: restart a worker, lose one GPU in a test environment if possible, and verify that requests fail clearly rather than hanging.
- Repeat after changes: a runtime upgrade, new quantization or altered context limit invalidates an old capacity result.
Keep the benchmark record with GPU type, count, region, machine shape, runtime version, date and price assumptions. This makes a later provider comparison meaningful instead of mixing measurements from incompatible configurations.
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Common mistakes and fixes
Choosing by parameter count
Symptom: an instance is selected because its GPU is marketed for a certain model size, then fails at the desired context or concurrency. Fix: size the exact precision, context, runtime and KV cache, and validate usable rather than nominal memory.
Using the largest card without measuring
Symptom: the model works, but cost per token is poor because the GPU is lightly utilized. Fix: compare a smaller profile, batching and concurrency at the same latency objective.
Assuming aggregate memory guarantees speed
Symptom: a multi-GPU machine has enough total memory but poor throughput or high tail latency. Fix: verify topology, interconnect and the runtime’s parallelism implementation with the target model.
Treating an hourly price as total cost
Symptom: the invoice exceeds the quote because storage, egress, idle time or attached host resources were omitted. Fix: calculate fully loaded cost and convert it to measured cost per token.
Ignoring capacity until launch day
Symptom: the chosen GPU is listed but cannot be created in the required region or account. Fix: check quota and capacity, and test the fallback profile before publishing a delivery date.
Decision checklist
- Exact model checkpoint and tokenizer recorded.
- Precision or quantization fixed and supported by the runtime.
- Maximum context and concurrency written as numeric targets.
- Usable GPU memory verified with runtime overhead and KV-cache headroom.
- Single- versus multi-GPU topology justified by capacity or measured performance.
- Host RAM, local SSD, network path and region checked.
- Quota, reservation and current capacity confirmed.
- Benchmark run with production-like input/output lengths and concurrency.
- Latency percentiles, throughput, errors and memory recorded.
- Fully loaded cost per token calculated, with a tested fallback.
The defensible choice is therefore not a particular GPU brand or tier. It is the available instance shape that passes your model’s usable-memory test, delivers the required latency and throughput at realistic concurrency, and has a measured, fully loaded cost that fits the service budget.
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