Data center GPU · NVIDIA
What AI models can a Tesla V100 32GB run?
32 GB puts the 70B class within comfortable reach and leaves room for the context window that makes those models worth running. At 900 GB/s there is enough bandwidth to keep generation responsive for anything that fits.
The short answer
Assuming an 8k context window and default settings, these are the models worth downloading first.
Best all-rounder
Gemma 4 31B Instruct
Q6_K · 27.61 GB · about 24.4 tokens/s
Best for code
Qwen3 32B
Q6_K · 26.86 GB · about 25.1 tokens/s
Largest that still runs well
Mixtral 8x7B Instruct V0.1
46.7B parameters · Q4_K_M · about 46.7 tokens/s
Fastest useful answer
LFM2.5 8B A1B
about 233.1 tokens/s · 8.81 GB
See how fast it feels
Tesla V100 32GB running Gemma 4 31B Instruct at Q6_K
YouWhy does my model use more memory when the conversation gets longer?
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Where it sits
With 32 GB it belongs to the 32 GB tier. The 32 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 900 GB/s decides how fast it answers.
Our indicative price for this card is $1,400 used, reviewed 2026-09-01. That is 2.3 GB of memory and 64 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.
Devices in a similar price band, fastest memory first:
| Device | Price | Memory | Bandwidth | Best all-round pick |
|---|---|---|---|---|
| GeForce RTX 5080 | $1,050 | 16 GB | 960 GB/s | Qwen3.5 27B, 51.3 t/s |
| RTX A5000 | $1,200 used | 24 GB | 768 GB/s | Qwen3.5 27B, 27.1 t/s |
Every model, scored on this device
Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.
| Model | Params | Verdict | Download | Memory used | Speed | Max context |
|---|---|---|---|---|---|---|
| Gemma 4 31B Instruct Gemma | 31.27B | Runs well | Q6_K | 27.61 GB | 24.4 t/s | 16k |
| Qwen3.5 27B Qwen | 27.78B | Runs well | Q8_0 | 27.32 GB | 24.6 t/s | 64k |
| Qwen3 32B Qwen | 32.76B | Runs great | Q6_K | 26.86 GB | 25.1 t/s | 16k |
| Granite 4.1 30B Granite | 28.87B | Just fits | Q8_0 | 30.97 GB | 21.7 t/s | 8k |
| Qwen2.5 32B Instruct Qwen | 32.76B | Runs great | Q6_K | 26.86 GB | 25.1 t/s | 16k |
| Qwen2.5 Coder 32B Instruct Qwen | 32.76B | Runs great | Q6_K | 26.86 GB | 25.1 t/s | 16k |
| Gemma 3 27B Instruct Gemma | 27.43B | Runs well | Q8_0 | 28.68 GB | 23.5 t/s | 32k |
| Gemma 4 26B A4B Instruct Gemma | 25.81B (3.74B active) | Runs great | Q8_0 | 26.72 GB | 84.9 t/s | 64k |
| Qwen3.5 35B A3B Qwen | 35.95B (2.9B active) | Runs great | Q6_K | 27.62 GB | 160.1 t/s | 128k |
| GLM 4.7 Flash GLM | 31.22B (3.66B active) | Runs great | Q6_K | 24.35 GB | 120.8 t/s | 128k |
| Qwen3 30B A3B Qwen | 30.53B (3.34B active) | Just fits | Q8_0 | 30.99 GB | 95.5 t/s | 8k |
| Mistral Small 24B Instruct 2501 Mistral | 23.57B | Runs great | Q8_0 | 24.35 GB | 27.7 t/s | 32k |
| Gemma 4 12B Instruct Gemma | 11.96B | Runs great | Q8_0 | 13.55 GB | 50.4 t/s | 128k |
| Qwen3 14B Qwen | 14.77B | Runs great | Q8_0 | 15.54 GB | 43.9 t/s | 32k |
| GPT OSS 20B GPT-OSS | 20.91B (4.18B active) | Runs great | MXFP4 | 11.55 GB | 160.8 t/s | 128k |
| Qwen3 Coder 30B A3B Instruct Qwen | 30.53B (3.34B active) | Just fits | Q8_0 | 30.99 GB | 95.5 t/s | 8k |
| Qwen2.5 14B Instruct Qwen | 14.77B | Runs great | Q8_0 | 15.79 GB | 43.2 t/s | 32k |
| Qwen2.5 Coder 14B Instruct Qwen | 14.77B | Runs great | Q8_0 | 15.79 GB | 43.2 t/s | 32k |
| Codestral 22B V0.1 Mistral | 22.25B | Runs great | Q8_0 | 24.04 GB | 28.1 t/s | 32k |
| Qwen3.5 9B Qwen | 9.65B | Runs great | Q8_0 | 9.25 GB | 74.9 t/s | 256k |
| Phi 3.5 MoE Instruct Phi | 41.87B (6.64B active) | Runs great | Q5_K_M | 29.05 GB | 71.3 t/s | 16k |
| Mixtral 8x7B Instruct V0.1 Mistral | 46.7B (12.88B active) | Runs great | Q4_K_M | 27.59 GB | 46.7 t/s | 32k |
| Phi 4 Phi | 14.66B | Runs great | Q8_0 | 16 GB | 42.6 t/s | 16k |
| Llama 3.3 70B Instruct Llama | 70.55B | Fits only at 2 to 3 bits | IQ3_XXS ! | 27.8 GB | 24.3 t/s | 16k |
| Qwen2.5 VL 72B Instruct Qwen | 73.41B | Fits only at 2 to 3 bits | IQ3_XXS ! | 28.75 GB | 23.5 t/s | 8k |
| Gemma 3 12B Instruct Gemma | 12.19B | Runs great | Q8_0 | 13.27 GB | 51.4 t/s | 128k |
| Qwen2.5 72B Instruct Qwen | 72.71B | Fits only at 2 to 3 bits | IQ3_XXS ! | 28.5 GB | 23.7 t/s | 16k |
| Gemma 4 E4B Instruct Gemma | 8B | Runs great | Q8_0 | 8.42 GB | 82.1 t/s | 128k |
| Granite 4.1 8B Granite | 8.79B | Runs great | Q8_0 | 10.35 GB | 66.6 t/s | 128k |
| Qwen3 8B Qwen | 8.19B | Runs great | Q8_0 | 9.02 GB | 76.9 t/s | 32k |
Show the other 56 models
| Mistral Nemo Instruct 2407 | 12.25B | Runs great | Q8_0 | 13.15 GB | 52.1 t/s | 64k |
| Qwen2.5 VL 7B Instruct | 8.29B | Runs great | Q8_0 | 8.49 GB | 81.7 t/s | 64k |
| Qwen2.5 7B Instruct | 7.62B | Runs great | Q8_0 | 7.83 GB | 89 t/s | 32k |
| Llama 3.1 8B Instruct | 8.03B | Runs great | Q8_0 | 8.83 GB | 78.6 t/s | 128k |
| DeepSeek Coder V2 Lite Instruct | 15.71B | Runs great | Q8_0 | 15.91 GB | 131.9 t/s | 128k |
| Gemma 2 9B Instruct | 9.24B | Runs great | Q8_0 | 11.5 GB | 59.6 t/s | 8k |
| Qwen2.5 Coder 7B Instruct | 7.62B | Runs great | Q8_0 | 7.83 GB | 89 t/s | 32k |
| OLMo 3 7B Instruct | 7.3B | Runs great | Q8_0 | 9.72 GB | 71.1 t/s | 64k |
| Internlm3 8B Instruct | 8.8B | Runs great | Q8_0 | 8.97 GB | 77.3 t/s | 32k |
| Mistral 7B Instruct V0.3 | 7.25B | Runs great | Q8_0 | 8.45 GB | 82.3 t/s | 32k |
| Gemma 4 E2B Instruct | 5.12B | Runs great | Q8_0 | 5.44 GB | 129.4 t/s | 128k |
| Qwen3.5 4B | 4.66B | Runs great | Q8_0 | 5.27 GB | 134.9 t/s | 256k |
| Mistral 7B Instruct V0.2 | 7.24B | Runs great | Q8_0 | 8.44 GB | 82.4 t/s | 32k |
| DeepSeek Coder 7B Instruct V1.5 | 6.91B | Runs great | Q8_0 | 10.58 GB | 65.1 t/s | 4k |
| DeepSeek Coder 6.7B Instruct | 6.74B | Runs great | Q8_0 | 10.94 GB | 62.8 t/s | 16k |
| LFM2.5 8B A1B | 8.47B | Runs great | Q8_0 | 8.81 GB | 233.1 t/s | 64k |
| Qwen3 4B | 4.02B | Runs great | Q8_0 | 5.46 GB | 129.8 t/s | 32k |
| Gemma 3 4B Instruct | 4.3B | Runs great | Q8_0 | 4.91 GB | 145.5 t/s | 128k |
| Phi 4 Mini Instruct | 3.84B | Runs great | Q8_0 | 5.17 GB | 138 t/s | 128k |
| Granite 4.1 3B | 3.4B | Runs great | Q8_0 | 4.34 GB | 166 t/s | 128k |
| Qwen2.5 3B Instruct | 3.09B | Runs great | Q8_0 | 3.68 GB | 198.3 t/s | 32k |
| Phi 3 Mini 4k Instruct | 3.82B | Runs great | Q8_0 | 4.8 GB | 149.4 t/s | 4k |
| Llama 3.2 3B Instruct | 3.21B | Runs great | Q8_0 | 4.42 GB | 163.4 t/s | 128k |
| LFM2.5 2.6B | 2.7B | Runs great | Q8_0 | 3.14 GB | 236.8 t/s | 128k |
| Qwen3.5 2B | 2.27B | Runs great | Q8_0 | 2.7 GB | 280.8 t/s | 256k |
| Gemma 2 2B Instruct | 2.61B | Runs great | Q8_0 | 3.54 GB | 207.4 t/s | 8k |
| Starcoder2 3B | 3.03B | Runs great | Q8_0 | 3.34 GB | 223.2 t/s | 16k |
| Qwen3 1.7B | 2.03B | Runs great | Q8_0 | 3.22 GB | 229.6 t/s | 32k |
| DeepSeek R1 Distill Qwen 1.5B | 1.78B | Runs great | Q8_0 | 2.07 GB | 378.6 t/s | 128k |
| Qwen2.5 1.5B Instruct | 1.54B | Runs great | Q8_0 | 2.06 GB | 380 t/s | 32k |
| SmolLM2 1.7B | 1.71B | Runs great | Q8_0 | 3.53 GB | 207.4 t/s | 8k |
| OLMo 2 0425 1B | 1.48B | Runs great | Q8_0 | 2.6 GB | 292.8 t/s | 4k |
| Llama 3.2 1B Instruct | 1.24B | Runs great | Q8_0 | 1.82 GB | 448.3 t/s | 128k |
| LFM2.5 1.2B Instruct | 1.17B | Runs great | Q8_0 | 1.46 GB | 591.9 t/s | 64k |
| Gemma 3 1B Instruct | 1B | Runs great | Q8_0 | 1.35 GB | 637.4 t/s | 32k |
| Qwen3.5 0.8B | 0.87B | Runs great | Q8_0 | 1.28 GB | 680.7 t/s | 256k |
| Qwen3 0.6B | 0.75B | Runs great | Q8_0 | 1.92 GB | 409.5 t/s | 32k |
| Qwen2.5 0.5B Instruct | 0.49B | Runs great | Q8_0 | 0.88 GB | 1144.4 t/s | 32k |
| LFM2.5 350M | 0.35B | Runs great | Q8_0 | 0.68 GB | 1772 t/s | 64k |
| Qwen3 Coder Next | 79.67B | Partial offload | n/a | 45.22 GB | 75 t/s | n/a |
| Qwen3 Next 80B A3B Instruct | 81.32B | Partial offload | n/a | 46.15 GB | 72 t/s | n/a |
| GPT OSS 120B | 116.83B | Partial offload | n/a | 59.4 GB | 28.9 t/s | n/a |
| Qwen3.5 122B A10B | 125.09B | Partial offload | n/a | 70.61 GB | 20.1 t/s | n/a |
| GLM 4.5 Air | 110.47B | Partial offload | n/a | 63.61 GB | 11.8 t/s | n/a |
| MiniMax M2.5 | 228.7B | Partial offload | n/a | 115.16 GB | 10.1 t/s | n/a |
| MiniMax M2.7 | 228.69B | Partial offload | n/a | 115.15 GB | 10.1 t/s | n/a |
| Qwen3 235B A22B | 235.09B | Partial offload | n/a | 133.71 GB | 4.3 t/s | n/a |
| MiniMax M3 | 427.04B | Partial offload | n/a | 240.84 GB | 4.2 t/s | n/a |
| GLM 4.5 | 358.34B | Partial offload | n/a | 204.37 GB | 3.1 t/s | n/a |
| Kimi K2.6 | 1026.88B | Partial offload | n/a | 577.78 GB | 2.5 t/s | n/a |
| Kimi K2 Instruct | 1026.41B | Partial offload | n/a | 577.52 GB | 2.5 t/s | n/a |
| DeepSeek V3.2 | 685.4B | Partial offload | n/a | 385.91 GB | 2.5 t/s | n/a |
| DeepSeek R1 | 684.53B | Partial offload | n/a | 385.42 GB | 2.5 t/s | n/a |
| GLM 5.1 | 753.86B | Partial offload | n/a | 424.51 GB | 2.3 t/s | n/a |
| GLM 5.2 | 753.33B | Partial offload | n/a | 424.21 GB | 2.2 t/s | n/a |
| Llama 3 3 Nemotron Super 49B V1 | 49.87B | Partial offload | n/a | 47.99 GB | 1.6 t/s | n/a |
What it will not run
0 of the 86 architectures we track are out of reach here, even at 2-bit precision. Another 17 run by splitting the model between the card and system RAM, which works but drops generation to single digits for most dense models.
If you want more headroom
The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.
| Device | Memory | Bandwidth | Models that fit | |
|---|---|---|---|---|
| RTX 6000 Ada Generation | 48 GB | 960 GB/s | 72 | Check price |
| Radeon PRO W7900 | 48 GB | 864 GB/s | 72 | Check price |
| RTX A6000 | 48 GB | 768 GB/s | 72 | Check price |
| Ryzen AI Max+ 395 64GB | 64 GB | 256 GB/s | 66 | Check price |
Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.
Other devices with 32 GB
Same capacity, different speed. Once a model fits, bandwidth is what separates these.
Questions people ask about a Tesla V100 32GB
How much VRAM does a Tesla V100 32GB have?
The Tesla V100 32GB has 32 GB of VRAM, of which roughly 31.2 GB is available to a model once the operating system takes its share.
Can a Tesla V100 32GB run a 70B model?
Yes. At IQ3_XXS a 70B model fits in 27.8 GB and generates around 24.3 tokens per second.
What is the best local model for a Tesla V100 32GB?
Gemma 4 31B Instruct at Q6_K is the strongest all-round fit: 27.61 GB used, around 24.4 tokens per second at 8k context.