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

92 models fit comfortably on NVIDIA GeForce RTX 3080 16GB (Mobile) with 32 GB system RAM. 62 more run with tradeoffs. Top pick: Qwen 3 0.6B.

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 chat?

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
92 models

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

#1Qwen 3 0.6B

0.6B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 13.6 GBTTFT: instant

919

tok/s

Estimated

Weights

0.30 GB

KV cache

0.30 GB

Activations

0.02 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~61 ms (instant)

Model details →

#2Qwen 3 1.7B

1.7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GBTTFT: fast

324

tok/s

Estimated

Weights

0.90 GB

KV cache

0.85 GB

Activations

0.05 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~174 ms (fast)

Model details →

#3TinyLlama 1.1B Chat v1.0

1.1B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GBTTFT: fast

501

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~113 ms (fast)

Model details →

#4Gemma 2 2B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GBTTFT: fast

276

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~205 ms (fast)

Model details →

#5Kumru 2B

2.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 11.4 GBTTFT: fast

ollama run alibayram/kumru:latest

230

tok/s

Estimated

Weights

1.50 GB

KV cache

1.20 GB

Activations

0.08 GB

Runtime

1.80 GB

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

Model details →

#6Falcon 3 3B Instruct

falcon

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.1 GBHeadroom: 10.9 GBTTFT: fast

184

tok/s

Estimated

Weights

1.70 GB

KV cache

1.50 GB

Activations

0.09 GB

Runtime

1.80 GB

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

Model details →

#7Dolphin 3.0 Llama 3.2 3B

dolphin

Commercial OK

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

184

tok/s

Estimated

Weights

1.80 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 →

#8Hermes 3 Llama 3.2 3B

hermes

Commercial OK

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

184

tok/s

Estimated

Weights

1.80 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 →

#9Granite 3.0 2B Instruct

granite

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 3.9 GBHeadroom: 12.1 GBTTFT: fast

276

tok/s

Estimated

Weights

1.50 GB

KV cache

0.50 GB

Activations

0.08 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~205 ms (fast)

Model details →

#10Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GBTTFT: fast

ollama run llama3.2:3b

104

tok/s

Estimated

Weights

3.40 GB

KV cache

1.50 GB

Activations

0.18 GB

Runtime

1.80 GB

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

Model details →

#11Mistral Turkish v2 (brooqs)

7.2B

mistral

Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: noticeable

ollama run brooqs/mistral-turkish-v2:latest

tok/s

Estimated

Weights

4.10 GB

KV cache

3.60 GB

Activations

0.21 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~737 ms (noticeable)

Model details →

#12Trendyol LLM 7B Chat v0.1

llama

Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 8.4 GBTTFT: noticeable

tok/s

Estimated

Weights

3.90 GB

KV cache

1.75 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.

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 →

Gemma 2 9B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable

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

ollama run gemma2:9b

tok/s

Estimated

Weights

5.80 GB

KV cache

4.50 GB

Activations

0.30 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~922 ms (noticeable)

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 →

Gemma 3 12B

12B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GBTTFT: noticeable

• Tight VRAM fit — only 0.5 GB headroom left for context growth

ollama run gemma3:12b

tok/s

Estimated

Weights

7.30 GB

KV cache

6.00 GB

Activations

0.37 GB

Runtime

1.80 GB

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

Model details →

Stable LM 2 12B

12B

other

Quant: Q4_K_MContext: 4,096VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable

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

tok/s

Estimated

Weights

7.20 GB

KV cache

3.00 GB

Activations

0.36 GB

Runtime

1.80 GB

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

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 →

Mistral Small 3 24B

24B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 6.7 GBTTFT: slow

• Partial CPU offload: ~44% of layers run on CPU

ollama run mistral-small:24b

tok/s

Estimated

Weights

14.00 GB

KV cache

12.00 GB

Activations

0.71 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2458 ms (slow)

Model details →

Mistral Small 3.2 24B

24B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 28.5 GBHeadroom: 6.7 GBTTFT: slow

• Partial CPU offload: ~44% of layers run on CPU

tok/s

Estimated

Weights

14.00 GB

KV cache

12.00 GB

Activations

0.71 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2458 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: 5 new comfortable, 78 new tradeoff

• Gemma 3 270M
• SmolLM2 135M Instruct
• SmolLM2 360M Instruct
• VBART Large (Turkish Summarization)

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: 24 new comfortable

• Qwen 3 14B
• Gemma 3 270M
• Phi-4 14B
• Phi-4 Reasoning 14B

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: 55 new comfortable

• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen 3 32B
• Gemma 4 31B Dense

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