FitMyLLM
NVIDIA· VOLTA

NVIDIA Tesla V100 DGXS 32 GB

Running LLMs on the Tesla V100 DGXS 32 GB — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
32 GB
HIGH-END
BANDWIDTH
897
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
~239
TOK/S
7B
~103
TOK/S
14B
~51
TOK/S
32B
~22
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
897 GB/s
FP16 COMPUTE
31.3 TFLOPS
TDP
250W
MEMORY
HBM2
ARCHITECTURE
Volta
CUDA CORES
5,120
TENSOR CORES
640
PCIE
Gen 3 x16
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 Tesla V100 DGXS 32 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· 337
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
7973
TOK/S · 2% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
5316
TOK/S · 2% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
5126
TOK/S · 2% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
5126
TOK/S · 2% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2803
TOK/S · 2% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2658
TOK/S · 2% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2353
TOK/S · 2% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
2142
TOK/S · 2% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
2142
TOK/S · 2% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
2142
TOK/S · 2% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
2050
TOK/S · 2% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1993
TOK/S · 2% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1888
TOK/S · 2% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1435
TOK/S · 2% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1435
TOK/S · 2% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1380
TOK/S · 3% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1263
TOK/S · 3% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1263
TOK/S · 3% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1196
TOK/S · 3% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1196
TOK/S · 3% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1196
TOK/S · 3% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1157
TOK/S · 3% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
970
TOK/S · 3% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
957
TOK/S · 3% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
886
TOK/S · 3% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
825
TOK/S · 3% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
763
TOK/S · 3% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
664
TOK/S · 4% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
652
TOK/S · 4% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
598
TOK/S · 4% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
598
TOK/S · 4% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
598
TOK/S · 4% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
598
TOK/S · 4% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
552
TOK/S · 4% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
552
TOK/S · 4% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
552
TOK/S · 4% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
532
TOK/S · 4% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
505
TOK/S · 4% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
505
TOK/S · 4% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
478
TOK/S · 17% VRAM
▸ NEXT STEP

Get personalized recommendations.

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

WHAT THIS CARD IS WORTH

Tesla V100 DGXS 32 GB 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
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
what memory costs on this card
THE BIGGEST IT TAKES
Phi-3.5 MoE 42B41.9B · Q4_K_M28.6 GB97 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
Tesla V100 DGXS 32 GB32 GB

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 Tesla V100 DGXS 32 GB — 32 GB VRAM.

TESLA V100 DGXS 32 GB SPEC
BRAND
NVIDIA
VRAM
32 GB HBM2
BANDWIDTH
897 GB/s
FP16 COMPUTE
31.3 TFLOPS
FP32 COMPUTE
15.7 TFLOPS
CUDA CORES
5,120
TENSOR CORES
640
TDP
250 W
ARCHITECTURE
Volta
▸ AI CAPABILITY
337/ 449 models @ Q4

With 32 GB VRAM and 897 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 TESLA V100 DGXS 32 GB
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 GB23953.3
Qwen 3.6 35B A3B35B21.9 GB23953.9
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