FitMyLLM
NVIDIA· BLACKWELL

NVIDIA B200

Running LLMs on the B200 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $30,000 buys

VRAM
90 GB
FLAGSHIP
BANDWIDTH
4100
GB/S
MODELS Q4
390/449
87%
7B Q4 SPEED
~469
BLAZING
▸ MODEL COVERAGE @ Q487% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~1093
TOK/S
7B
~469
TOK/S
14B
~234
TOK/S
32B
~103
TOK/S
70B
~47
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
90 GB
BANDWIDTH
4100 GB/s
FP16 COMPUTE
1191.2 TFLOPS
TDP
1000W
MEMORY
HBM3e
ARCHITECTURE
Blackwell
CUDA CORES
18,944
TENSOR CORES
592
PCIE
Gen 5 x16
MSRP
$30,000
387
FAST MODELS · >30 TOK/S
Real-time chat speed
390
USABLE · >10 TOK/S
Comfortable for all tasks
390
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying B200 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· 390
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
36444
TOK/S · 1% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
24296
TOK/S · 1% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
23429
TOK/S · 1% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
23429
TOK/S · 1% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
12812
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
12148
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
10754
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
9791
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
9791
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
9791
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
9371
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
9111
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
8632
TOK/S · 1% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
6560
TOK/S · 1% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
6560
TOK/S · 1% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
6308
TOK/S · 1% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
5775
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
5775
TOK/S · 1% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
5467
TOK/S · 1% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
5467
TOK/S · 1% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
5467
TOK/S · 1% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
5290
TOK/S · 1% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
4432
TOK/S · 1% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
4373
TOK/S · 1% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
4049
TOK/S · 1% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
3770
TOK/S · 1% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
3489
TOK/S · 1% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
3037
TOK/S · 1% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
2982
TOK/S · 1% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
2733
TOK/S · 1% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
2733
TOK/S · 1% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
2733
TOK/S · 1% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
2733
TOK/S · 1% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
2523
TOK/S · 1% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
2523
TOK/S · 1% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
2523
TOK/S · 1% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
2430
TOK/S · 1% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
2310
TOK/S · 2% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
2310
TOK/S · 2% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
2187
TOK/S · 6% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your B200.

WHAT THIS CARD IS WORTH

B200 holds 390 of the models in our catalogue and is, in practice, a Q4_K_S card — the largest it takes is DBRX 132B at Q4_K_S.

TOKENS/SEC PER $100
0.1
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.30 GB
what memory costs on this card
THE BIGGEST IT TAKES
DBRX 132B132B · Q4_K_S80.2 GB6 tok/sEST
Mistral Medium 3.5128B · Q4_K_S78.6 GB2 tok/sEST
Pixtral Large 124B124B · Q4_K_M79.7 GB2 tok/sEST
Nemotron 3 Super 120B-A12B123.61B · Q4_K_M78.4 GB17 tok/sEST
Mistral-Large 123B123B · Q4_K_M79.1 GB2 tok/sEST
Devstral 2 123B123B · Q4_K_M79.1 GB2 tok/sEST
Qwen 3.5 122B A10B122B · Q4_K_M77.2 GB20 tok/sEST
Nemotron 3 Super 120B120B · Q4_K_M77.1 GB17 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
Instinct MI300A120 GB$12,0004.7 tok/s per $100
B20090 GB$30,0000.1 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 B200 — 90 GB VRAM.

B200 SPEC
BRAND
NVIDIA
VRAM
90 GB HBM3e
BANDWIDTH
4100 GB/s
FP16 COMPUTE
1191.2 TFLOPS
FP32 COMPUTE
74.5 TFLOPS
CUDA CORES
18,944
TENSOR CORES
592
TDP
1000 W
ARCHITECTURE
Blackwell
MSRP
$30000
▸ AI CAPABILITY
390/ 449 models @ Q4

With 90 GB VRAM and 4100 GB/s bandwidth, this GPU handles models up to 124B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~469 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR B200
390 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Pixtral Large 124B124B76.3 GB2639.3
Nemotron 3 Super 120B-A12B123.61B76.0 GB27353.2
Mistral-Large 123B123B75.7 GB2733.5
Devstral 2 123B123B75.7 GB2738.1
Qwen 3.5 122B A10B122B75.1 GB32856.8
Nemotron 3 Super 120B120B73.8 GB27357.3
Mistral Small 4 119B119B73.2 GB50550.2
GPT-OSS 120B117B72.0 GB64354.1
Command A 111B111B68.3 GB3027.6
GLM 4.5 Air110B67.7 GB27351.0
Qwen 1.5 110B110B67.7 GB3033.4
Llama 4 Scout 17B-16E109B67.1 GB19333.9
Cogito v2 109B MoE109B67.1 GB193
Ling 2.6 Flash107.49B66.2 GB44336.8
Sarvam 105B105B64.7 GB3148.0
Command-R+ 104B104B64.1 GB3252.7
Llama-3.2-90B-Vision-Instruct90B55.5 GB3648.5
Hunyuan A13B80B49.4 GB25281.1
Qwen3-Coder-Next80B49.4 GB109343.0
Qwen3-Next 80B A3B80B49.4 GB109349.0