RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
RUNLOCALAI

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Some links on this site are affiliate links (Amazon Associates and other first-class retailers). When you buy through them, we earn a small commission at no extra cost to you. Affiliate links do not influence our verdicts — there are cards we rate highly that we don't have affiliate relationships with, and cards that sell well that we refuse to recommend. Read more →

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

What can NVIDIA GeForce RTX 4080 Super run for creative?

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
46 models

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

#1Hermes 3 Llama 3.1 8B
8B
hermes
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
ollama run hermes3:8b
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 →
#2Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 10.7 GBHeadroom: 5.3 GBTTFT: fast
ollama run gemma2:9b
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 →
#3CodeGemma 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 →
#4Moondream 2
1.9B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 5.3 GBHeadroom: 10.7 GBTTFT: instant
417
tok/s
E
Weights
1.15 GB
KV cache
0.24 GB
Activations
2.11 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~93 ms (instant)
Model details →Run-on benchmark page →
#5Whisper Large v3
1.55B
other
Commercial OK
Quant: FP16Context: 0VRAM: 5.1 GBHeadroom: 10.9 GBTTFT: instant
154
tok/s
E
Weights
3.10 GB
KV cache
0.00 GB
Activations
0.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~76 ms (instant)
Model details →Run-on benchmark page →
#6SmolLM 2 1.7B Instruct
1.7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.9 GBHeadroom: 4.1 GBTTFT: instant
466
tok/s
E
Weights
1.03 GB
KV cache
0.85 GB
Activations
8.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~83 ms (instant)
Model details →Run-on benchmark page →
#7Nemotron 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 →
#8Qwen 2.5 1.5B Instruct
1.5B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.7 GBHeadroom: 4.3 GBTTFT: instant
528
tok/s
E
Weights
0.91 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~74 ms (instant)
Model details →Run-on benchmark page →
#9Granite 3.0 2B Instruct
2B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 7.7 GBHeadroom: 8.3 GBTTFT: instant
396
tok/s
E
Weights
1.21 GB
KV cache
0.50 GB
Activations
4.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~98 ms (instant)
Model details →Run-on benchmark page →
#10DeepSeek R1 Distill Qwen 1.5B
1.5B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.7 GBHeadroom: 4.3 GBTTFT: instant
528
tok/s
E
Weights
0.91 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~74 ms (instant)
Model details →Run-on benchmark page →
#11Qwen 2.5 Coder 1.5B
1.5B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.7 GBHeadroom: 4.3 GBTTFT: instant
528
tok/s
E
Weights
0.91 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~74 ms (instant)
Model details →Run-on benchmark page →
#12RWKV 7 'Goose' 1.5B
1.5B
rwkv
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBTTFT: instant
464
tok/s
E
Weights
1.03 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~74 ms (instant)
Model details →Run-on benchmark page →

Runs with tradeoffs
73 models

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

Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 2.6 GBTTFT: fast
  • • Tight VRAM fit — only 2.6 GB headroom left for context growth
264
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →Run-on benchmark page →
Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 2.6 GBTTFT: fast
  • • Tight VRAM fit — only 2.6 GB headroom left for context growth
264
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →Run-on benchmark page →
Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GBTTFT: fast
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma4:e4b
198
tok/s
E
Weights
2.42 GB
KV cache
2.00 GB
Activations
8.31 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~196 ms (fast)
Model details →Run-on benchmark page →
Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GBTTFT: fast
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma3:4b
198
tok/s
E
Weights
2.42 GB
KV cache
2.00 GB
Activations
8.31 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~196 ms (fast)
Model details →Run-on benchmark page →
Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 2.8 GBTTFT: instant
  • • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run gemma4:e2b
225
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~98 ms (instant)
Model details →Run-on benchmark page →
Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 1.2 GBTTFT: fast
  • • Tight VRAM fit — only 1.2 GB headroom left for context growth
ollama run llama3.2:3b
150
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →Run-on benchmark page →
Qwen 3 4B
4B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GBTTFT: fast
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:4b
198
tok/s
E
Weights
2.42 GB
KV cache
2.00 GB
Activations
8.31 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~196 ms (fast)
Model details →Run-on benchmark page →
Phi-3.5 Mini Instruct
3.8B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.3 GBHeadroom: 1.7 GBTTFT: fast
  • • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run phi3.5:3.8b
209
tok/s
E
Weights
2.29 GB
KV cache
1.90 GB
Activations
8.31 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~186 ms (fast)
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: 7 new comfortable, 82 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Whisper Large v3 Turbo
  • • BGE M3
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: 46 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: 63 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.