What can NVIDIA GeForce RTX 3080 16GB (Mobile) run locally?

69 models fit comfortably on NVIDIA GeForce RTX 3080 16GB (Mobile) with 32 GB system RAM. 62 more run with tradeoffs. Top pick: Ministral 3B Instruct.

What's the recommended runner for this hardware?

llama.cpp / Ollama (CUDA). This is the runner that gives best tokens-per-second on NVIDIA GeForce RTX 3080 16GB (Mobile) based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 3080 16GB (Mobile) run for long context?

Build: NVIDIA GeForce RTX 3080 16GB (Mobile) + — + 32 GB RAM (windows)

Memory: 16 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
69 models

Ranked by fit for long context use case + predicted speed. Click a row for VRAM breakdown.

#1Ministral 3B Instruct

mistral

Quant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 10.7 GBTTFT: fast

184

tok/s

Estimated

Weights

1.90 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~307 ms (fast)

Model details →

#2Phi-3.5 Mini Instruct

3.8B

phi

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.0 GBHeadroom: 8.0 GBTTFT: fast

ollama run phi3.5:3.8b

tok/s

Estimated

Weights

4.10 GB

KV cache

1.90 GB

Activations

0.21 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~389 ms (fast)

Model details →

#3Codestral Mamba 7B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: noticeable

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

#4Falcon Mamba 7B

falcon

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: noticeable

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

#5Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBTTFT: fast

ollama run gemma4:e4b

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~410 ms (fast)

Model details →

#6Qwen 3 8B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: noticeable

ollama run qwen3:8b

tok/s

Estimated

Weights

4.80 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~819 ms (noticeable)

Model details →

#7Command R7B (12-2024)

command-r

Quant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 5.6 GBTTFT: noticeable

tok/s

Estimated

Weights

4.40 GB

KV cache

4.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~819 ms (noticeable)

Model details →

#8Ministral 8B Instruct

mistral

Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBTTFT: noticeable

tok/s

Estimated

Weights

5.00 GB

KV cache

4.00 GB

Activations

0.26 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~819 ms (noticeable)

Model details →

#9Qwen 2.5 7B Instruct

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: noticeable

ollama run qwen2.5:7b

tok/s

Estimated

Weights

8.10 GB

KV cache

0.47 GB

Activations

0.41 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

#10Llama 3.1 8B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBTTFT: noticeable

ollama run llama3.1:8b

tok/s

Estimated

Weights

8.50 GB

KV cache

1.07 GB

Activations

0.43 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~819 ms (noticeable)

Model details →

#11InternLM 2.5 7B Chat

internlm

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: noticeable

tok/s

Estimated

Weights

4.40 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

#12Turkish Mistral 7B Instruct v0.2

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.4 GBHeadroom: 6.6 GBTTFT: noticeable

tok/s

Estimated

Weights

3.90 GB

KV cache

3.50 GB

Activations

0.20 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~717 ms (noticeable)

Model details →

Runs with tradeoffs
62 models

Tight VRAM, partial CPU offload, or context-limited.

Nemotron 3 Nano (30B-A3B)

30B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 10.7 GBTTFT: slow

• Partial CPU offload: ~35% of layers run on CPU

ollama run nemotron3:nano

tok/s

Estimated

Weights

18.00 GB

KV cache

3.75 GB

Activations

0.90 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~3072 ms (slow)

Model details →

Mistral Nemo 12B Instruct

12B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.7 GBHeadroom: 0.3 GBTTFT: noticeable

• Tight VRAM fit — only 0.3 GB headroom left for context growth

ollama run mistral-nemo:12b

tok/s

Estimated

Weights

7.50 GB

KV cache

6.00 GB

Activations

0.38 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1229 ms (noticeable)

Model details →

DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GBTTFT: fast

• Tight VRAM fit — only 3.2 GB headroom left for context growth

230

tok/s

Estimated

Weights

8.60 GB

KV cache

1.96 GB

Activations

0.43 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~246 ms (fast)

Model details →

Qwen 3 14B

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run qwen3:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

1.75 GB

Activations

0.42 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)

Model details →

Qwen 2.5 14B Instruct

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.9 GBHeadroom: 3.1 GBTTFT: noticeable

• Tight VRAM fit — only 3.1 GB headroom left for context growth

ollama run qwen2.5:14b

tok/s

Estimated

Weights

8.90 GB

KV cache

1.75 GB

Activations

0.45 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1434 ms (noticeable)

Model details →

Gemma 4 Turkish 26B (4B active)

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 29.8 GBHeadroom: 5.4 GBTTFT: slow

• Partial CPU offload: ~46% of layers run on CPU

tok/s

Estimated

Weights

14.30 GB

KV cache

13.00 GB

Activations

0.72 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2662 ms (slow)

Model details →

Gemma 3 27B

27B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 32.1 GBHeadroom: 3.1 GBTTFT: slow

• Partial CPU offload: ~50% of layers run on CPU

ollama run gemma3:27b

tok/s

Estimated

Weights

16.00 GB

KV cache

13.50 GB

Activations

0.81 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2765 ms (slow)

Model details →

Qwen3.8 27B

27B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 32.6 GBHeadroom: 2.6 GBTTFT: slow

• Partial CPU offload: ~51% of layers run on CPU

ollama run qwen3.8:27b

tok/s

Estimated

Weights

16.50 GB

KV cache

13.50 GB

Activations

0.83 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2765 ms (slow)

Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

+32 GB system RAM

Check the current price

Adds 32 GB to your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 28 new comfortable, 78 new tradeoff

• Qwen 3 0.6B
• Qwen 3 1.7B
• Gemma 3 270M
• SmolLM2 135M Instruct

Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

Check the current price

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~512 GB/s to ~616 GB/s.

Unlocks: 47 new comfortable

• Qwen 3 0.6B
• Qwen 3 1.7B
• Qwen 3 14B
• Gemma 3 270M

Add a second NVIDIA GeForce RTX 3080 16GB (Mobile)

Check the current price

Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.

Unlocks: 78 new comfortable

• Qwen 3 0.6B
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen 3 32B

Shop this upgrade↗

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)

1600B

deepseek

Commercial OK