RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
RUNLOCALAI

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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 4080 Super / coding

What can NVIDIA GeForce RTX 4080 Super run for coding?

Build: NVIDIA GeForce RTX 4080 Super + — + 32 GB RAM (windows)

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
38 models

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

#1CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run codegemma:7b
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#2Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run llama3.1:8b
99
tok/s
E
Weights
4.83 GB
KV cache
0.27 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#3Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
ollama run qwen3:8b
99
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#4StarCoder 2 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#5Qwen 2.5 Coder 7B Instruct
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run qwen2.5-coder:7b
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#6Codestral Mamba 7B
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#7OpenCoder 8B
8B
opencoder
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
99
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →Run-on benchmark page →
#8Yi Coder 9B
9B
yi
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fast
88
tok/s
E
Weights
5.43 GB
KV cache
1.13 GB
Activations
2.32 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~441 ms (fast)
Model details →Run-on benchmark page →
#9Nemotron Mini 4B Instruct
4B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 9.4 GBHeadroom: 6.6 GBTTFT: fast
198
tok/s
E
Weights
2.42 GB
KV cache
1.00 GB
Activations
4.22 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~196 ms (fast)
Model details →Run-on benchmark page →
#10DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run deepseek-r1:7b
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#11Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
ollama run mistral:7b
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
#12CodeQwen 1.5 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBTTFT: fast
113
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →

Runs with tradeoffs
71 models

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

DeepSeek Coder V2 Lite (16B)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 16.0 GBHeadroom: 0.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.0 GB headroom left for context growth
ollama run deepseek-coder-v2:16b
50
tok/s
E
Weights
9.66 GB
KV cache
2.00 GB
Activations
2.53 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~785 ms (noticeable)
Model details →Run-on benchmark page →
Codestral 22B
22B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 34.9 GBHeadroom: 0.3 GBTTFT: noticeable
  • • Partial CPU offload: ~54% of layers run on CPU
ollama run codestral:22b
36
tok/s
E
Weights
13.28 GB
KV cache
11.00 GB
Activations
8.86 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1079 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GBTTFT: fast
  • • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run qwen2.5:7b
99
tok/s
E
Weights
4.81 GB
KV cache
0.47 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →Run-on benchmark page →
Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 1.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:14b
57
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~687 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 1.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen2.5:14b
57
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~687 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 32.4 GBHeadroom: 2.8 GBTTFT: noticeable
  • • Partial CPU offload: ~51% of layers run on CPU
ollama run qwen2.5-coder:32b
25
tok/s
E
Weights
19.32 GB
KV cache
2.15 GB
Activations
9.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1569 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.6 GBHeadroom: 8.6 GBTTFT: noticeable
  • • Partial CPU offload: ~40% of layers run on CPU
ollama run qwen3:30b
26
tok/s
E
Weights
18.11 GB
KV cache
3.75 GB
Activations
2.95 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1471 ms (noticeable)
Model details →Run-on benchmark page →
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 27.4 GBHeadroom: 7.8 GBTTFT: noticeable
  • • Partial CPU offload: ~42% of layers run on CPU
ollama run gemma4:31b
26
tok/s
E
Weights
18.72 GB
KV cache
3.88 GB
Activations
2.98 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1520 ms (noticeable)
Model details →Run-on benchmark page →

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: 15 new comfortable, 82 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Moondream 2
  • • Whisper Large v3 Turbo
Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 3090 Ti

~$1199

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

Unlocks: 54 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
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: 71 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

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 V4 Flash (284B MoE)
284B
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

M
Measured — we ran this exact combo on owner hardware.

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

E
Estimated — pure formula based on VRAM bandwidth and model architecture.

Full methodology →

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