Best GPU for LLM Inference in 2026
LLM inference is fundamentally different from training: you care about latency (time to first token), throughput (tokens/second), and cost per token — not raw TFLOPS. Large VRAM lets you avoid quantization and serve bigger batches.
TL;DR
For production inference: MI300X for large models (70B+) due to 192GB VRAM. H100 for smaller models with best ecosystem support. L40S for the lowest $/token on models up to 30B. H200 if you need VRAM + speed.
TOP 5 GPUS RANKED
AMD Instinct MI300X
AMDTOP PICKBest for large model inference — 192GB VRAM
Memory
192GB HBM3
FP8 TFLOPS
2,614 TFLOPS
TDP
750W
Cloud Cost
~$3.20/hr
Pros
- +192GB VRAM: serve 70B at FP16 without quantization on a single GPU
- +30–40% lower cloud cost vs H100
- +Excellent with vLLM ROCm backend and TGI
- +High batch throughput for 7B–13B models
Cons
- −vLLM/TGI support good but fewer optimizations than CUDA builds
- −Flash Attention 2 ROCm port has minor overhead vs CUDA
NVIDIA H100 SXM5
NVIDIABest ecosystem for production inference
Memory
80GB HBM3
FP8 TFLOPS
3,958 TFLOPS
TDP
700W
Cloud Cost
~$2.50–3.50/hr
Pros
- +TensorRT-LLM: best-in-class inference optimization
- +FP8 inference with minimal quality loss (transformer engines)
- +Widest operator support for custom model architectures
- +vLLM, TGI, Triton all heavily optimized for H100
Cons
- −80GB limits batch size for 70B+ models
- −More expensive than MI300X for equivalent throughput
NVIDIA L40S
NVIDIABest $/token for models up to 34B
Memory
48GB GDDR6
FP8 TFLOPS
733 TFLOPS
TDP
350W
Cloud Cost
~$1.40/hr
Pros
- +Lowest cloud cost per token for 7B–34B models
- +GDDR6 memory = much cheaper per GPU than HBM
- +350W TDP fits in standard rack density
- +Excellent for high-concurrency chatbot serving
Cons
- −48GB limits you to models ≤34B at FP16 (single GPU)
- −No HBM — lower bandwidth limits throughput for large batches
NVIDIA H200 SXM
NVIDIASpeed + VRAM for demanding inference workloads
Memory
141GB HBM3e
FP8 TFLOPS
3,958 TFLOPS
TDP
700W
Cloud Cost
~$4.50/hr
Pros
- +141GB fits 70B at FP16 on a single GPU
- +4.8 TB/s bandwidth = fast KV cache loading
- +Better latency than H100 for large-batch inference
- +Full CUDA TensorRT-LLM support
Cons
- −Most expensive option per hour
- −MI300X is better value for pure large-model serving
NVIDIA A100 SXM4
NVIDIABudget workhorse for proven inference at scale
Memory
80GB HBM2e
FP8 TFLOPS
312 TFLOPS
TDP
400W
Cloud Cost
~$1.80/hr
Pros
- +Widely available, competitive spot pricing
- +80GB for 70B inference with INT8 quantization
- +Mature vLLM/TGI/TensorRT-LLM support
- +Good for stable, predictable inference loads
Cons
- −Older architecture — lower efficiency than H100/H200
- −FP16 throughput ~5× lower than H100
KEY FACTORS TO CONSIDER
VRAM determines what you can serve without quantization
A 70B model at FP16 needs ~140GB VRAM. With INT8 quantization: ~70GB. INT4: ~35GB. Quantization reduces memory but also slightly reduces quality. MI300X (192GB) serves 70B at FP16 on a single GPU; H100 (80GB) needs 2 GPUs or INT8.
Throughput vs latency trade-off
High-throughput serving (batch many requests) benefits from high TFLOPS. Low-latency serving (single user, fast response) benefits from high memory bandwidth. H100/H200 excel at both; L40S is throughput-optimized for smaller models.
KV cache size limits concurrent users
The KV cache grows with context length × batch size × model size. Running 1,000 concurrent 4K-context requests on a 70B model requires ~50GB+ KV cache alone. More VRAM = more concurrent users = better economics.
FREQUENTLY ASKED QUESTIONS
How many tokens per second can an H100 serve for Llama 3 70B?
With 2× H100 (tensor parallel, 160GB total) using vLLM + FP8: approximately 1,200–2,000 tokens/second throughput at batch size 32. Single-user latency: ~80 tokens/second. Numbers vary by prompt length, context, and serving framework.
Is it worth using FP8 quantization for inference?
Yes for most use cases. FP8 on H100/H200 (using Transformer Engine) typically loses <1% on standard benchmarks (MMLU, HellaSwag) while doubling throughput and halving VRAM usage. Sensitive tasks like code generation may see 1–2% degradation.
MI300X vs H100 for inference — which is more cost-effective?
MI300X wins on $/token for 70B+ models due to 192GB VRAM (no tensor parallelism needed) and lower hourly cost. H100 wins for models under 30B where its TensorRT-LLM optimizations are battle-tested. For mixed workloads, H100 is safer.
What is the cheapest way to run LLM inference in 2026?
L40S at ~$1.40/hr handles 7B–34B models well. For 70B, MI300X at ~$3.20/hr on Lambda or CoreWeave. Use spot instances for 40–60% additional savings on non-latency-critical batch workloads.
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