FitMyLLM
NVIDIA· HOPPER

NVIDIA H100 SXM5 64 GB

Running LLMs on the H100 SXM5 64 GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $25,000 buys

VRAM
64 GB
FLAGSHIP
BANDWIDTH
2020
GB/S
MODELS Q4
373/449
83%
7B Q4 SPEED
~231
BLAZING
▸ MODEL COVERAGE @ Q483% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

Average speeds at Q4 quantization. Actual performance varies by model architecture and context length.

3B
~539
TOK/S
7B
~231
TOK/S
14B
~115
TOK/S
32B
~51
TOK/S
70B
~23
TOK/S
▸ MEASURED RIG REPORTS

We rent the machine and time every model on it: decode, VRAM peak, concurrency, watts and cost per million tokens. Yours may already be one of them — and two are free to read in full.

SEE THE REPORTS
▸ SPECIFICATIONS
VRAM
64 GB
BANDWIDTH
2020 GB/s
FP16 COMPUTE
267.6 TFLOPS
TDP
700W
MEMORY
HBM3
ARCHITECTURE
Hopper
CUDA CORES
16,896
TENSOR CORES
528
PCIE
Gen 5 x16
MSRP
$25,000
347
FAST MODELS · >30 TOK/S
Real-time chat speed
373
USABLE · >10 TOK/S
Comfortable for all tasks
373
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying H100 SXM5 64 GB costs $15–$40k and isn’t practical for most teams. Spin one up by the hour instead:

Spin up in ~60s. Pay by the second. Cancel anytime.

Some links are affiliate links — we may earn a small commission at no extra cost to you. This helps keep FitMyLLM free and independent.

▸ COMPATIBLE MODELS· 373
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
17956
TOK/S · 1% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
11970
TOK/S · 1% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
11543
TOK/S · 1% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
11543
TOK/S · 1% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
6312
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
5985
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
5298
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
4824
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
4824
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
4824
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
4617
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
4489
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
4253
TOK/S · 1% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
3232
TOK/S · 1% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
3232
TOK/S · 1% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
3108
TOK/S · 1% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
2845
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2845
TOK/S · 1% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
2693
TOK/S · 1% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
2693
TOK/S · 1% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
2693
TOK/S · 1% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
2606
TOK/S · 1% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
2184
TOK/S · 1% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
2155
TOK/S · 1% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1995
TOK/S · 2% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1857
TOK/S · 2% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1719
TOK/S · 2% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
1496
TOK/S · 2% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
1469
TOK/S · 2% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
1347
TOK/S · 2% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
1347
TOK/S · 2% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1347
TOK/S · 2% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
1347
TOK/S · 2% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
1243
TOK/S · 2% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
1243
TOK/S · 2% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
1243
TOK/S · 2% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
1197
TOK/S · 2% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
1138
TOK/S · 2% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
1138
TOK/S · 2% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1077
TOK/S · 9% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your H100 SXM5 64 GB.

WHAT THIS CARD IS WORTH

H100 SXM5 64 GB holds 373 of the models in our catalogue and is, in practice, a Q4_K_S card — the largest it takes is Llama-3.2-90B-Vision-Instruct at Q4_K_S.

TOKENS/SEC PER $100
0.2
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.26 GB
what memory costs on this card
THE BIGGEST IT TAKES
Llama-3.2-90B-Vision-Instruct90B · Q4_K_S56.3 GB3 tok/sEST
Hunyuan A13B80B · Q4_K_M51.9 GB23 tok/sEST
Qwen3-Coder-Next80B · Q4_K_M51.5 GB99 tok/sEST
Qwen3-Next 80B A3B80B · Q4_K_M51.5 GB99 tok/sEST
NVLM-D 72B79.38B · Q4_K_M52.3 GB4 tok/sEST
InternVL3 78B78B · Q4_K_M51.4 GB4 tok/sEST
Qwen2.5-72B72.7B · Q5_K_M55.5 GB4 tok/sEST
Qwen2-VL 72B72.7B · Q5_K_M55.5 GB4 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 6000D84 GB$7,5001.9 tok/s per $100
H100 SXM5 80GB80 GB$25,0000.1 tok/s per $100
H100 CNX80 GB$25,0000.2 tok/s per $100
A100 SXM 80GB80 GB$10,0000.4 tok/s per $100
H100 SXM5 64 GB64 GB$25,0000.2 tok/s per $100

Neighbours in memory rather than in price: memory decides whether a card can do the job at all, so two cards of the same size at different prices is the comparison you are making. Ordered by memory, then bandwidth — not by the value column, which is worked out from bandwidth and price alone and therefore rewards a cheap card whatever its software stack does to that bandwidth in practice. Read it as one input, not as a ranking.

▸ DEVICE UNDER TEST

NVIDIA H100 SXM5 64 GB — 64 GB VRAM.

H100 SXM5 64 GB SPEC
BRAND
NVIDIA
VRAM
64 GB HBM3
BANDWIDTH
2020 GB/s
FP16 COMPUTE
267.6 TFLOPS
FP32 COMPUTE
66.9 TFLOPS
CUDA CORES
16,896
TENSOR CORES
528
TDP
700 W
ARCHITECTURE
Hopper
MSRP
$25000
▸ AI CAPABILITY
373/ 449 models @ Q4

With 64 GB VRAM and 2020 GB/s bandwidth, this GPU handles models up to 80B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~231 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR H100 SXM5 64 GB
373 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Hunyuan A13B80B49.4 GB12481.1
Qwen3-Coder-Next80B49.4 GB53943.0
Qwen3-Next 80B A3B80B49.4 GB53949.0
NVLM-D 72B79.38B49.0 GB2048.7
InternVL3 78B78B48.2 GB2180.6
Qwen2.5-72B72.7B44.9 GB2239.7
Qwen2-VL 72B72.7B44.9 GB2255.5
Qwen 1.5 72B72B44.5 GB2249.7
Qwen2 Math 72B72B44.5 GB2249.7
Molmo 72B72B44.5 GB2254.1
DeepSeek R1 Distill Llama 70B70.6B43.6 GB2342.4
Llama 3.3 70B70.6B43.6 GB2344.8
Llama 3.1 70B70.6B43.6 GB2333.2
Llama 3 70B70.6B43.6 GB2344.1
Llama-3.1-Nemotron-70B70.6B43.6 GB2343.7
Cogito 70B70B43.3 GB23
Llama 2 70B70B43.3 GB2333.4
CodeLlama 70B70B43.3 GB2345.7
Dolphin Llama 3 70B70B43.3 GB2345.7
Tulu 3 70B70B43.3 GB2359.4