Choose an inference accelerator in this order: define the model’s latency and throughput target, eliminate hardware that cannot hold the weights and KV cache, benchmark the remaining candidates with your exact serving stack, then compare cost per delivered token under the required service level. Memory capacity is the first gate; a fast GPU that cannot fit the workload is not an option.
Start with the service you must deliver
An accelerator decision is an infrastructure decision, not a specification-sheet contest. The same model can favor different hardware depending on context length, concurrency, batching, precision and the latency objective. Write down the production target before looking at GPU names.
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Record these workload variables
- Model: family, parameter count, architecture and version.
- Precision: FP16, BF16, FP8, INT8, INT4 or another quantization format, including any layers that remain at higher precision.
- Context: maximum input length and expected output length. Longer contexts increase KV-cache memory.
- Concurrency: peak simultaneous requests and the traffic pattern during bursts.
- Time to first token (TTFT): how quickly a request must begin producing output.
- End-to-end latency: the p50, p95 and p99 limits, not just an average.
- Throughput: requests per second and generated tokens per second.
- Batching policy: maximum batch size, continuous batching behavior and whether latency-sensitive requests share a batch with long generations.
- Availability target: redundancy, failover time and the number of nodes that may be unavailable during maintenance.
A target such as “run a 70B model” is incomplete. “Serve a 70B model at BF16, 8,192-token context, 32 concurrent users, p95 TTFT below 500 ms and at least 200 generated tokens per second” is a benchmarkable requirement.
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Weights and KV cache must fit in accelerator memory with room for runtime allocations, temporary activations and fragmentation. AWS’s inference guidance recommends estimating both components and removing hardware that fails this test before measuring speed. Its example capacities are 22 GB for L4, 44 GB for L40S, 80 GB for H100, 141 GB for H200, 180 GB for B200 and 268 GB for B300.
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Estimate weight memory
A rough lower bound is parameter count multiplied by bytes per parameter. A 70-billion-parameter model therefore needs about 140 GB for 16-bit weights before overhead. Eight-bit weights need about 70 GB, and four-bit weights about 35 GB, but quantization metadata, unquantized layers and runtime buffers add to those figures. The exact footprint depends on the model and serving implementation, so treat the arithmetic as a screening estimate rather than a capacity guarantee.
Account for KV-cache growth
Autoregressive serving stores key and value tensors for every layer, sequence position and active request. The cache grows with context length and concurrency. A deployment that fits a model with one short prompt can run out of memory when several users submit long conversations. Reserve headroom for the largest supported context and peak concurrent sessions, not the median request.
Use the capacity table as an elimination tool
| Accelerator example | Memory listed in AWS example | What a 90 GB requirement means |
|---|---|---|
| L4 | 22 GB | Cannot fit on one GPU |
| L40S | 44 GB | Cannot fit on one GPU |
| H100 | 80 GB | Cannot fit on one GPU |
| H200 | 141 GB | Passes the single-GPU capacity screen |
| B200 | 180 GB | Passes the single-GPU capacity screen |
| B300 | 268 GB | Passes the single-GPU capacity screen |
The 90 GB example is deliberately simple: it eliminates single-GPU L4, L40S and H100 configurations regardless of their throughput. If the model is split across GPUs, add tensor- or pipeline-parallel communication and confirm that the resulting topology still meets latency targets. More GPUs do not automatically compensate for insufficient interconnect bandwidth.
Compare the main accelerator options
| Option | Evidence-based strengths | Decision cautions | Best fit |
|---|---|---|---|
| NVIDIA H100 | 3 TB/s memory bandwidth per GPU; NVLink and NVSwitch support for scale-out; broad TensorRT, Triton and NIM ecosystem. | 80 GB-class memory can restrict large models. Price and supply must be checked for the date and market where you will deploy. | General-purpose production inference where software maturity and multi-GPU scaling are priorities. |
| NVIDIA H200 | AWS’s example lists 141 GB, providing substantially more capacity than an H100 for models that exceed 80 GB. | Requires a compatible platform, PCIe layout and current availability. | Larger models that fit in one or fewer GPUs while retaining the NVIDIA software stack. |
| AMD Instinct MI300X | 192 GB HBM3 and 5.325 TB/s peak bandwidth, according to AMD Performance Labs (2023). AMD reports single-GPU Llama 2 70B support and nearly linear ROCm scaling in its MLPerf material. | Validate ROCm kernels, framework support and operator coverage for the exact model, quantization and serving version. | Memory-heavy inference for teams prepared to operate and tune ROCm. |
| Intel Gaudi 2 | Intel’s MLPerf Inference v4.0 submission (2024) reports Llama 2 70B results and emphasizes price-performance. The cited Hugging Face TGI submission uses continuous batching and tensor parallelism. | Confirm model coverage, framework integration, local supply and support arrangements before standardizing. | Cost-sensitive deployments with supported models and in-house Intel software expertise. |
These categories are not universal rankings. Vendor submissions use specific model versions, software, batch settings and system configurations. They demonstrate what a platform achieved under those conditions, not what every deployment will achieve.
What the published figures actually say
- NVIDIA lists 3 TB/s memory bandwidth for H100 on its current product information.
- AMD Performance Labs reported 192 GB HBM3 and 5.325 TB/s peak bandwidth for MI300X in 2023.
- Intel’s MLPerf Inference v4.0 submission reported 8,035.0 offline and 6,287.5 server tokens per second for Gaudi 2 on Llama 2 70B in 2024. Those are benchmark results for the submitted configuration.
- Intel reported a 1.42x geometric-mean improvement for 5th Generation Xeon over 4th Generation Xeon in MLPerf Inference v3.1 (2024); that is a CPU-generation comparison, not a universal accelerator multiplier.
- AMD’s MLPerf Inference 4.1 analysis placed MI300X within 2–3% of the cited DGX H100 configuration in FP8 server and offline scenarios. The comparison is tied to the stated benchmark setup.
Benchmark practical serving performance
After the memory gate, benchmark the exact model and software path you intend to operate. AWS notes that a model can fit in memory and still miss latency or throughput objectives.
Build a representative test
- Load the production model revision, tokenizer and quantization files.
- Use the intended inference engine and versions, such as your chosen compiler, runtime and API server.
- Reproduce realistic prompt and output-length distributions, including the longest supported context.
- Run several concurrency levels rather than one maximum batch. Include low-load interactive traffic and peak bursts.
- Measure TTFT, inter-token latency, end-to-end p50, p95 and p99 latency, requests per second, generated tokens per second, queue time and batch occupancy.
- Repeat with warm caches and after cold starts. Record startup time and memory use during model loading.
- Test failure and recovery: restart a worker, remove a GPU, drain a node and restore service. Record the impact on SLOs.
Interpret the results correctly
High aggregate tokens per second can hide unacceptable TTFT or tail latency. Conversely, a low-latency configuration may waste capacity if it cannot batch enough requests. Plot throughput against concurrency and identify the point where p95 or p99 latency crosses its limit. That point, rather than the vendor’s maximum number, determines useful capacity.
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For multi-GPU inference, measure communication time and scaling efficiency. Compare one, two, four and eight GPUs when the model allows it. Near-linear scaling is valuable, but it is workload-dependent; synchronization, KV-cache movement and PCIe or fabric limits can flatten the curve.
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Optimize cost per delivered work
Compare accelerators by the cost of meeting the SLO, not by purchase price or peak throughput alone. Include host servers, networking, storage, power, cooling, software support, utilization and the number of replicas required for availability.
Use relative figures only as a starting point
AWS provides an illustrative relative table in which L4 is 1.0x throughput at 1.0x cost, L40S is 2.5x throughput at 1.7x cost, H100 is 3.5x throughput at 3.0x cost and H200 is 3.8x throughput at 3.5x cost. These ratios are examples, not universal prices or performance guarantees; workload, region, utilization and software versions can change the result.
| Example accelerator | Relative throughput | Relative cost | How to use the figure |
|---|---|---|---|
| L4 | 1.0x | 1.0x | Baseline only; still must pass memory and SLO tests. |
| L40S | 2.5x | 1.7x | Potentially better value when 44 GB is sufficient. |
| H100 | 3.5x | 3.0x | Evaluate when software maturity or scale-out offsets its higher cost. |
| H200 | 3.8x | 3.5x | Memory headroom may eliminate the need for multiple GPUs. |
Calculate cost per delivered token or request at the measured operating point. If a larger-memory GPU lets you serve the model on one device instead of two, include the avoided server, networking and operational costs. If a card has low utilization because the service is latency-bound, its theoretical throughput does not improve economics.
Check topology and server design
The accelerator is one part of a system. NVIDIA’s platform guidance recommends balanced two-, four- or eight-GPU systems, host RAM of at least twice the aggregate GPU memory, PCIe links appropriate to the GPU, and balanced placement across CPU root ports.
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System requirements to verify
- Host memory: at least 2× aggregate GPU memory as a planning baseline, with additional capacity for model loading, preprocessing and observability.
- PCIe and fabric: confirm generation, lane width, NUMA placement and peer-to-peer paths. A physically present link may not provide the bandwidth your parallelism strategy expects.
- Networking: NVIDIA recommends at least 200 Gbps for multi-node inference. Check switch, NIC, topology and congestion-control support as a complete path.
- Storage: use NVMe for model weights, caches and logs where startup time and sustained I/O matter.
- Management: require Redfish-capable management, remote firmware updates, health telemetry and a tested out-of-band recovery process.
- Security: verify TPM 2.0, secure boot, signed firmware and a process for rotating credentials and applying driver updates.
- Power and cooling: validate rack power, peak draw, airflow, liquid-cooling requirements where applicable and the facility’s ability to sustain thermal limits.
For a production cluster, map GPU-to-NIC and GPU-to-CPU affinity before installation. An unbalanced root-port arrangement can create bottlenecks that do not appear in a single-card test.
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Evaluate software and operational fit
Software coverage often decides whether advertised hardware performance is reachable. Check framework support, optimized kernels, quantization formats, compiler maturity, collective-communication libraries, monitoring integrations and the serving stack your team can maintain.
Questions for each candidate
- Does the runtime support every model operator, attention implementation and quantization format you require?
- Are continuous batching, tensor parallelism, speculative decoding and prefix caching available and stable?
- How quickly are drivers, firmware and framework versions released, and who owns compatibility testing?
- Can you expose GPU memory, temperature, power, ECC or equivalent error data, queue depth and per-request latency to your monitoring system?
- What happens when a GPU, host, fabric link or software worker fails?
- Is there an OEM or integrator support path for replacement parts and firmware defects?
MI300X can be compelling when its large memory avoids sharding, but the team must validate ROCm operators and the chosen serving stack. Gaudi 2 can suit price-sensitive deployments when the model and TGI integration are supported. H100 and H200 offer a broad NVIDIA ecosystem, but capacity and platform cost still need to be tested against the target.
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Prioritize low queueing delay, fast prompt processing and predictable p95/p99 latency. Benchmark short and long prompts separately, cap batch size when necessary, and reserve capacity for bursts. A card with higher peak throughput is not automatically better if batching pushes TTFT beyond the SLO.
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Prioritize sustained tokens per second, efficient large batches and utilization. Tail latency matters less than completion time and cost per token, so a platform that scales efficiently across several GPUs may beat a lower-latency single-card option.
Large model with limited sharding tolerance
Start with memory-rich accelerators. MI300X’s 192 GB HBM3, H200’s 141 GB example capacity, B200’s 180 GB and B300’s 268 GB can reduce or eliminate tensor parallelism for models that exceed 80 GB. Confirm that weights plus KV cache and runtime overhead fit; capacity numbers alone do not prove a deployment will run.
Cost-sensitive, supported model
Screen L4, L40S and Gaudi 2 where the model fits and the software path is mature. Compare measured cost per SLO-compliant request, including the number of cards needed, rather than selecting the lowest purchase price.
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Multi-node production service
Favor a balanced server design, fast interconnects, at least 200 Gbps networking for the cited NVIDIA guidance, and operational tooling your team already understands. Test node loss, rolling upgrades and cross-node traffic before committing to a fleet size.
Procurement checklist
- Write the model, precision, maximum context, concurrency and TTFT, p95/p99 latency and throughput targets.
- Calculate weight and KV-cache memory for peak conditions, then add runtime headroom.
- Eliminate accelerators that fail the single-card or planned multi-card memory requirement.
- Obtain a representative system with the exact GPU, host, fabric and software versions.
- Run reproducible tests at realistic prompt lengths and concurrency levels.
- Record throughput, TTFT, tail latency, utilization, power, startup time and scaling efficiency.
- Price the complete system or rental, including host, network, storage, power, cooling, support and redundancy.
- Review driver, firmware, security, observability and failure-recovery procedures.
- Repeat the benchmark after every major model, runtime or driver change.
Common mistakes and fixes
Buying by model name alone
Symptom: the selected GPU cannot load the model at the required context or concurrency. Fix: perform the memory gate first and include KV-cache growth.
Using vendor benchmark numbers as a promise
Symptom: production throughput is far below a published result. Fix: reproduce the same model, precision, batch policy, framework and hardware topology, then benchmark your traffic distribution.
Optimizing average latency
Symptom: users experience timeouts despite an acceptable mean. Fix: make p95 and p99 latency explicit SLOs and test burst behavior.
Ignoring host and network bottlenecks
Symptom: GPUs show low utilization while CPU, PCIe or inter-node links are saturated. Fix: verify NUMA and root-port balance, peer-to-peer paths, NIC affinity and network capacity.
Underestimating operations
Symptom: a failed card causes a long outage or an upgrade breaks the serving stack. Fix: require health telemetry, Redfish management, secure-boot controls, tested rollback and spare or replacement procedures.
Bottom line
There is no universal best inference accelerator. Pick the least expensive, operationally supportable system that passes the memory gate and meets measured TTFT, tail-latency and throughput targets on your exact model. H100 is a strong general-purpose choice when its 80 GB capacity is sufficient and the NVIDIA stack reduces integration risk; H200 and other high-memory options are better starting points when the model or KV cache exceeds that limit; MI300X is attractive for memory-heavy workloads when ROCm coverage is proven; and Gaudi 2 deserves consideration where supported models and price-performance align. Re-test whenever the model, quantization, serving software or traffic profile changes.
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