What can NVIDIA GeForce RTX 4080 Super run locally?

75 models fit comfortably on NVIDIA GeForce RTX 4080 Super with 32 GB system RAM. 98 more run with tradeoffs. Top pick: Llama 3.1 Nemotron Nano 8B.

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 4080 Super based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 4080 Super run for reasoning?

Build: NVIDIA GeForce RTX 4080 Super + — + 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
75 models

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

#1Llama 3.1 Nemotron Nano 8B

llama

Commercial OK

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

tok/s

Estimated

Weights

4.83 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~392 ms (fast)

Model details →

#2RefinedNeuro RN TR R1

llama

Commercial OK

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

ollama run RefinedNeuro/RN_TR_R1:latest

tok/s

Estimated

Weights

4.83 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~392 ms (fast)

Model details →

#3DeepSeek R1 Distill Llama 8B

deepseek

Commercial OK

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

tok/s

Estimated

Weights

4.83 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~392 ms (fast)

Model details →

#4Qwen 2.5 Math 7B

qwen

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 8.0 GBHeadroom: 8.0 GBTTFT: fast

113

tok/s

Estimated

Weights

4.23 GB

KV cache

1.75 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

#5Qwen 3 7B

qwen

Commercial OK

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

113

tok/s

Estimated

Weights

4.23 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

#6InternLM 2.5 7B Chat

internlm

Commercial OK

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

113

tok/s

Estimated

Weights

4.23 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

#7EXAONE Deep 7.8B

7.8B

other

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

102

tok/s

Estimated

Weights

4.71 GB

KV cache

3.90 GB

Activations

0.24 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~383 ms (fast)

Model details →

#8RefinedNeuro RN TR R2

llama

Commercial OK

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

ollama run RefinedNeuro/RN_TR_R2:latest

tok/s

Estimated

Weights

4.83 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~392 ms (fast)

Model details →

#9Mistral Turkish v2 (brooqs)

7.2B

mistral

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

ollama run brooqs/mistral-turkish-v2:latest

118

tok/s

Estimated

Weights

4.05 GB

KV cache

3.60 GB

Activations

0.21 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~353 ms (fast)

Model details →

#10Trendyol LLM 7B Chat v0.1

llama

Quant: Q4_K_MContext: 4,096VRAM: 8.0 GBHeadroom: 8.0 GBTTFT: fast

113

tok/s

Estimated

Weights

4.23 GB

KV cache

1.75 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

#11Mistral 7B Instruct v0.2

mistral

Commercial OK

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

113

tok/s

Estimated

Weights

4.23 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

#12Turkish Mistral 7B Instruct v0.2

mistral

Commercial OK

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

113

tok/s

Estimated

Weights

4.23 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

Runs with tradeoffs
98 models

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

NVIDIA Nemotron Nano 9B v2 Japanese

other

Commercial OK

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

• Tight VRAM fit — only 4.0 GB headroom left for context growth

tok/s

Estimated

Weights

5.43 GB

KV cache

4.50 GB

Activations

0.28 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~441 ms (fast)

Model details →

DeepSeek R1 Distill Qwen 7B

deepseek

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 13.1 GBHeadroom: 2.9 GBTTFT: fast

• Tight VRAM fit — only 2.9 GB headroom left for context growth

ollama run deepseek-r1:7b

tok/s

Estimated

Weights

7.44 GB

KV cache

3.50 GB

Activations

0.38 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~343 ms (fast)

Model details →

Phi-4 Reasoning 14B

14B

phi

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 phi4-reasoning:14b

tok/s

Estimated

Weights

8.45 GB

KV cache

1.75 GB

Activations

0.42 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~687 ms (noticeable)

Model details →

DeepSeek R1 Distill Qwen 14B

14B

deepseek

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 deepseek-r1:14b

tok/s

Estimated

Weights

8.45 GB

KV cache

1.75 GB

Activations

0.42 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~687 ms (noticeable)

Model details →

DeepSeek V3 Lite (16B MoE)

16B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 13.9 GBHeadroom: 2.1 GBTTFT: fast

• Tight VRAM fit — only 2.1 GB headroom left for context growth

330

tok/s

Estimated

Weights

9.66 GB

KV cache

2.00 GB

Activations

0.49 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~118 ms (fast)

Model details →

DeepSeek R1 Distill Mistral 24B

24B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 29.0 GBHeadroom: 6.2 GBTTFT: noticeable

• Partial CPU offload: ~45% of layers run on CPU

tok/s

Estimated

Weights

14.49 GB

KV cache

12.00 GB

Activations

0.73 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1177 ms (noticeable)

Model details →

Omni 31B Turkish Reasoning

31B

other

Quant: Q4_K_MContext: 2,048VRAM: 25.3 GBHeadroom: 9.9 GBTTFT: noticeable

• Partial CPU offload: ~37% of layers run on CPU

tok/s

Estimated

Weights

18.72 GB

KV cache

3.88 GB

Activations

0.94 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1520 ms (noticeable)

Model details →

DeepSeek R1 Distill Qwen 32B

32B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 26.1 GBHeadroom: 9.1 GBTTFT: noticeable

• Partial CPU offload: ~39% of layers run on CPU

ollama run deepseek-r1:32b

tok/s

Estimated

Weights

19.32 GB

KV cache

4.00 GB

Activations

0.97 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1569 ms (noticeable)

Model details →

What if you upgraded?

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

+32 GB system RAM

~$80–150

Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 98 new comfortable, 108 new tradeoff

• all-MiniLM-L6-v2
• Piper
• Whisper Tiny
• Whisper Base

Shop this upgrade↗

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

~$350

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~736 GB/s to ~616 GB/s.

Unlocks: 131 new comfortable

• all-MiniLM-L6-v2
• Piper
• Whisper Tiny
• Whisper Base

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4080 Super

~$1099

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: 175 new comfortable

• all-MiniLM-L6-v2
• Piper
• Whisper Tiny
• Whisper Base

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

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

Qwen 3.5 235B-A17B (MoE)

397B

qwen

Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

Qwen 3 235B-A22B

235B

qwen

Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

DeepSeek R1 (671B reasoning)

671B

deepseek

Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

Llama 4 Scout

109B

llama

Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

Measured here

Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed

Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated

Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated

Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

RunLocalAI Will-It-Run Framework →

Want a specific benchmark we don't have? Email Contact support and we'll prioritize it.

Runs comfortably75 models

Runs with tradeoffs98 models

What if you upgraded?

+32 GB system RAM

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

Add a second NVIDIA GeForce RTX 4080 Super

Won't runtop 5 popular models

How to read these numbers

Runs comfortably
75 models

Runs with tradeoffs
98 models

Won't run
top 5 popular models