FitMyLLM
NVIDIA· HOPPER

NVIDIA H100 CNX

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

VRAM
80 GB
FLAGSHIP
BANDWIDTH
2040
GB/S
MODELS Q4
382/449
85%
7B Q4 SPEED
~233
BLAZING
▸ MODEL COVERAGE @ Q485% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~544
TOK/S
7B
~233
TOK/S
14B
~117
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
80 GB
BANDWIDTH
2040 GB/s
FP16 COMPUTE
215.4 TFLOPS
TDP
350W
MEMORY
HBM2e
ARCHITECTURE
Hopper
CUDA CORES
14,592
TENSOR CORES
456
PCIE
Gen 5 x16
MSRP
$25,000
351
FAST MODELS · >30 TOK/S
Real-time chat speed
382
USABLE · >10 TOK/S
Comfortable for all tasks
382
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying H100 CNX 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· 382
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
18133
TOK/S · 1% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
12089
TOK/S · 1% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
11657
TOK/S · 1% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
11657
TOK/S · 1% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
6375
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
6044
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
5351
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
4872
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
4872
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
4872
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
4663
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
4533
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
4295
TOK/S · 1% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
3264
TOK/S · 1% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
3264
TOK/S · 1% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
3138
TOK/S · 1% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
2873
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2873
TOK/S · 1% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
2720
TOK/S · 1% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
2720
TOK/S · 1% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
2720
TOK/S · 1% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
2632
TOK/S · 1% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
2205
TOK/S · 1% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
2176
TOK/S · 1% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
2015
TOK/S · 1% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1876
TOK/S · 1% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1736
TOK/S · 1% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
1511
TOK/S · 1% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
1484
TOK/S · 1% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
1360
TOK/S · 2% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
1360
TOK/S · 2% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1360
TOK/S · 2% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
1360
TOK/S · 2% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
1255
TOK/S · 2% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
1255
TOK/S · 2% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
1255
TOK/S · 2% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
1209
TOK/S · 2% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
1149
TOK/S · 2% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
1149
TOK/S · 2% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1088
TOK/S · 7% VRAM
▸ NEXT STEP

Get personalized recommendations.

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

WHAT THIS CARD IS WORTH

H100 CNX holds 382 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Nemotron 3 Super 120B-A12B at IQ4_XS.

TOKENS/SEC PER $100
0.2
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.32 GB
what memory costs on this card
THE BIGGEST IT TAKES
Nemotron 3 Super 120B-A12B123.61B · IQ4_XS71.8 GB27 tok/sEST
Qwen 3.5 122B A10B122B · IQ4_XS70.6 GB33 tok/sEST
Nemotron 3 Super 120B120B · IQ4_XS70.7 GB27 tok/sEST
Mistral Small 4 119B119B · IQ4_XS68.9 GB51 tok/sEST
GPT-OSS 120B117B · Q4_K_S70.9 GB62 tok/sEST
Command A 111B111B · Q4_K_M71.3 GB3 tok/sEST
GLM 4.5 Air110B · Q4_K_M70.5 GB25 tok/sEST
Qwen 1.5 110B110B · Q4_K_M71.0 GB3 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M1 Ultra (128GB)96 GB$4,9991.4 tok/s per $100
M2 Ultra (128GB)96 GB$3,9991.8 tok/s per $100
M4 Max (128GB)96 GB$3,9991.4 tok/s per $100
H100 PCIe 96 GB96 GB$25,0000.1 tok/s per $100
H100 CNX80 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 CNX — 80 GB VRAM.

H100 CNX SPEC
BRAND
NVIDIA
VRAM
80 GB HBM2e
BANDWIDTH
2040 GB/s
FP16 COMPUTE
215.4 TFLOPS
FP32 COMPUTE
53.8 TFLOPS
CUDA CORES
14,592
TENSOR CORES
456
TDP
350 W
ARCHITECTURE
Hopper
MSRP
$25000
▸ AI CAPABILITY
382/ 449 models @ Q4

With 80 GB VRAM and 2040 GB/s bandwidth, this GPU handles models up to 111B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~233 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR H100 CNX
382 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Command A 111B111B68.3 GB1527.6
GLM 4.5 Air110B67.7 GB13651.0
Qwen 1.5 110B110B67.7 GB1533.4
Llama 4 Scout 17B-16E109B67.1 GB9633.9
Cogito v2 109B MoE109B67.1 GB96
Ling 2.6 Flash107.49B66.2 GB22136.8
Sarvam 105B105B64.7 GB1648.0
Command-R+ 104B104B64.1 GB1652.7
Llama-3.2-90B-Vision-Instruct90B55.5 GB1848.5
Hunyuan A13B80B49.4 GB12681.1
Qwen3-Coder-Next80B49.4 GB54443.0
Qwen3-Next 80B A3B80B49.4 GB54449.0
NVLM-D 72B79.38B49.0 GB2148.7
InternVL3 78B78B48.2 GB2180.6
Qwen2.5-72B72.7B44.9 GB2239.7
Qwen2-VL 72B72.7B44.9 GB2255.5
Qwen 1.5 72B72B44.5 GB2349.7
Qwen2 Math 72B72B44.5 GB2349.7
Molmo 72B72B44.5 GB2354.1
DeepSeek R1 Distill Llama 70B70.6B43.6 GB2342.4