FitMyLLM
▸ NVIDIA· VOLTA

NVIDIA V100 SXM2 32GB

Running LLMs on the V100 SXM2 32GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $3,500 buys

VRAM
32 GB
HIGH-END
BANDWIDTH
900
GB/S
MODELS Q4
337/449
75%
7B Q4 SPEED
~103
BLAZING
▸ MODEL COVERAGE @ Q475% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~240
TOK/S
7B
~103
TOK/S
14B
~51
TOK/S
32B
~23
TOK/S
70B
—
39.4GB NEEDED
▸ 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
32 GB
BANDWIDTH
900 GB/s
FP16 COMPUTE
125.3 TFLOPS
TDP
300W
MEMORY
HBM2
ARCHITECTURE
Volta
CUDA CORES
5,120
TENSOR CORES
640
PCIE
Gen 3 x16
MSRP
$3,500
291
FAST MODELS · >30 TOK/S
Real-time chat speed
337
USABLE · >10 TOK/S
Comfortable for all tasks
337
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying V100 SXM2 32GB 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· 337
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
8000
TOK/S · 2% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
5333
TOK/S · 2% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
5143
TOK/S · 2% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
5143
TOK/S · 2% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2812
TOK/S · 2% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2667
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2361
TOK/S · 2% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
2149
TOK/S · 2% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
2149
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
2149
TOK/S · 2% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
2057
TOK/S · 2% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
2000
TOK/S · 2% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1895
TOK/S · 2% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1440
TOK/S · 2% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1440
TOK/S · 2% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1385
TOK/S · 3% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1268
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1268
TOK/S · 3% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1200
TOK/S · 3% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1200
TOK/S · 3% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1200
TOK/S · 3% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1161
TOK/S · 3% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
973
TOK/S · 3% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
960
TOK/S · 3% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
889
TOK/S · 3% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
828
TOK/S · 3% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
766
TOK/S · 3% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
667
TOK/S · 4% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
655
TOK/S · 4% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
600
TOK/S · 4% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
600
TOK/S · 4% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
600
TOK/S · 4% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
600
TOK/S · 4% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
554
TOK/S · 4% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
554
TOK/S · 4% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
554
TOK/S · 4% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
533
TOK/S · 4% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
507
TOK/S · 4% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
507
TOK/S · 4% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
480
TOK/S · 17% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your V100 SXM2 32GB.

WHAT THIS CARD IS WORTH

V100 SXM2 32GB holds 337 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Phi-3.5 MoE 42B at Q4_K_M.

TOKENS/SEC PER $100
2.5
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
0.91 GB
what memory costs on this card
THE BIGGEST IT TAKES
Phi-3.5 MoE 42B41.9B · Q4_K_M28.6 GB98 tok/sEST
Falcon 40B40B · Q4_K_M27.4 GB16 tok/sEST
InternVL3 38B38B · Q4_K_M26.7 GB17 tok/sEST
Seed-OSS 36B Instruct36B · Q5_K_S28.6 GB16 tok/sEST
c4ai-command-r-v01 35B35B · Q5_K_M28.1 GB16 tok/sEST
Qwen 3.5 35B A3B35B · Q5_K_M27.5 GB184 tok/sEST
Qwen 3.6 35B A3B35B · Q5_K_M27.5 GB184 tok/sEST
Nous Capybara 34B34.4B · Q5_K_M27.9 GB16 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M4 Max (48GB)36 GB$2,4992.2 tok/s per $100
M3 Max (48GB)36 GB$2,8991.6 tok/s per $100
M4 Pro (48GB)36 GB$1,7991.9 tok/s per $100
A100 SXM4 40 GB40 GB$10,0001.4 tok/s per $100
V100 SXM2 32GB32 GB$3,5002.5 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 V100 SXM2 32GB — 32 GB VRAM.

▸ V100 SXM2 32GB SPEC
BRAND
NVIDIA
VRAM
32 GB HBM2
BANDWIDTH
900 GB/s
FP16 COMPUTE
125.3 TFLOPS
FP32 COMPUTE
15.7 TFLOPS
CUDA CORES
5,120
TENSOR CORES
640
TDP
300 W
ARCHITECTURE
Volta
MSRP
$3500
▸ AI CAPABILITY
337/ 449 models @ Q4

With 32 GB VRAM and 900 GB/s bandwidth, this GPU handles models up to 41.9B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~103 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR V100 SXM2 32GB
337 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Phi-3.5 MoE 42B41.9B26.1 GB10956.7
Falcon 40B40B24.9 GB1820.9
InternVL3 38B38B23.7 GB1978.9
Seed-OSS 36B Instruct36B22.5 GB2054.4
c4ai-command-r-v01 35B35B21.9 GB2127.5
Qwen 3.5 35B A3B35B21.9 GB24053.3
Qwen 3.6 35B A3B35B21.9 GB24053.9
Nous Capybara 34B34.4B21.5 GB2142.0
Yi-1.5 34B34.4B21.5 GB2145.3
Falcon-H1 34B34B21.3 GB2166.1
CodeLlama 34B34B21.3 GB2125.4
Nous Hermes 2 34B34B21.3 GB2147.0
Phind CodeLlama 34B34B21.3 GB2168.1
LLaVA-1.6 Yi 34B34B21.3 GB2147.4
WizardCoder Python 34B34B21.3 GB2173.2
Yi 34B34B21.3 GB2133.4
Qwen3-VL 32B Instruct33.36B20.9 GB2244.6
DeepSeek Coder 33B33B20.7 GB2226.0
Vicuna 33B33B20.7 GB2217.2
LLaMA 1 30B33B20.7 GB2217.8