FitMyLLM
▸ NVIDIA· VOLTA

NVIDIA Quadro GV100

Running LLMs on the Quadro GV100 — 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
868
GB/S
MODELS Q4
337/449
75%
7B Q4 SPEED
~99
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
~231
TOK/S
7B
~99
TOK/S
14B
~50
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
868 GB/s
FP16 COMPUTE
33.3 TFLOPS
TDP
250W
MEMORY
HBM2
ARCHITECTURE
Volta
CUDA CORES
5,120
TENSOR CORES
640
PCIE
Gen 3 x16
285
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
▸ DON’T WANT TO BUY?

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▸ COMPATIBLE MODELS· 337
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
7716
TOK/S · 2% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
5144
TOK/S · 2% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
4960
TOK/S · 2% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
4960
TOK/S · 2% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
2713
TOK/S · 2% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
2572
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2277
TOK/S · 2% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
2073
TOK/S · 2% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
2073
TOK/S · 2% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
2073
TOK/S · 2% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
1984
TOK/S · 2% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
1929
TOK/S · 2% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
1827
TOK/S · 2% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
1389
TOK/S · 2% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
1389
TOK/S · 2% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
1335
TOK/S · 3% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
1223
TOK/S · 3% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
1223
TOK/S · 3% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
1157
TOK/S · 3% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
1157
TOK/S · 3% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
1157
TOK/S · 3% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
1120
TOK/S · 3% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
938
TOK/S · 3% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
926
TOK/S · 3% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
857
TOK/S · 3% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
798
TOK/S · 3% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
739
TOK/S · 3% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
643
TOK/S · 4% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
631
TOK/S · 4% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
579
TOK/S · 4% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
579
TOK/S · 4% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
579
TOK/S · 4% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
579
TOK/S · 4% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
534
TOK/S · 4% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
534
TOK/S · 4% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
534
TOK/S · 4% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
514
TOK/S · 4% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
489
TOK/S · 4% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
489
TOK/S · 4% VRAM
›
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
463
TOK/S · 17% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Quadro GV100.

WHAT THIS CARD IS WORTH

Quadro GV100 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 GB95 tok/sEST
Falcon 40B40B · Q4_K_M27.4 GB16 tok/sEST
InternVL3 38B38B · Q4_K_M26.7 GB16 tok/sEST
Seed-OSS 36B Instruct36B · Q5_K_S28.6 GB15 tok/sEST
c4ai-command-r-v01 35B35B · Q5_K_M28.1 GB15 tok/sEST
Qwen 3.5 35B A3B35B · Q5_K_M27.5 GB179 tok/sEST
Qwen 3.6 35B A3B35B · Q5_K_M27.5 GB179 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
Quadro GV10032 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 Quadro GV100 — 32 GB VRAM.

▸ QUADRO GV100 SPEC
BRAND
NVIDIA
VRAM
32 GB HBM2
BANDWIDTH
868 GB/s
FP16 COMPUTE
33.3 TFLOPS
FP32 COMPUTE
16.7 TFLOPS
CUDA CORES
5,120
TENSOR CORES
640
TDP
250 W
ARCHITECTURE
Volta
▸ AI CAPABILITY
337/ 449 models @ Q4

With 32 GB VRAM and 868 GB/s bandwidth, this GPU handles models up to 41.9B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~99 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR QUADRO GV100
337 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Phi-3.5 MoE 42B41.9B26.1 GB10556.7
Falcon 40B40B24.9 GB1720.9
InternVL3 38B38B23.7 GB1878.9
Seed-OSS 36B Instruct36B22.5 GB1954.4
c4ai-command-r-v01 35B35B21.9 GB2027.5
Qwen 3.5 35B A3B35B21.9 GB23153.3
Qwen 3.6 35B A3B35B21.9 GB23153.9
Nous Capybara 34B34.4B21.5 GB2042.0
Yi-1.5 34B34.4B21.5 GB2045.3
Falcon-H1 34B34B21.3 GB2066.1
CodeLlama 34B34B21.3 GB2025.4
Nous Hermes 2 34B34B21.3 GB2047.0
Phind CodeLlama 34B34B21.3 GB2068.1
LLaVA-1.6 Yi 34B34B21.3 GB2047.4
WizardCoder Python 34B34B21.3 GB2073.2
Yi 34B34B21.3 GB2033.4
Qwen3-VL 32B Instruct33.36B20.9 GB2144.6
DeepSeek Coder 33B33B20.7 GB2126.0
Vicuna 33B33B20.7 GB2117.2
LLaMA 1 30B33B20.7 GB2117.8