RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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RUNLOCALAI · v38
Will it run? / NVIDIA RTX 4090 48GB (China-mod) / creative

What can NVIDIA RTX 4090 48GB (China-mod) run for creative?

Build: NVIDIA RTX 4090 48GB (China-mod) + — + 32 GB RAM (windows)

Memory: 48 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
117 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: Q8_0Context: 8,192VRAM: 22.9 GBHeadroom: 25.1 GBTTFT: fast
ollama run hermes3:8b
77
tok/s
E
Weights
8.50 GB
KV cache
4.00 GB
Activations
8.62 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~248 ms (fast)
Model details →Run-on benchmark page →
#2Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 24.5 GBHeadroom: 23.5 GBTTFT: fast
ollama run gemma2:9b
69
tok/s
E
Weights
9.56 GB
KV cache
4.50 GB
Activations
8.67 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~279 ms (fast)
Model details →Run-on benchmark page →
#3Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 34.6 GBTTFT: instant
362
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): ~93 ms (instant)
Model details →Run-on benchmark page →
#4Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 34.6 GBTTFT: instant
362
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): ~93 ms (instant)
Model details →Run-on benchmark page →
#5Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 34.8 GBTTFT: instant
ollama run gemma4:e2b
308
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): ~62 ms (instant)
Model details →Run-on benchmark page →
#6Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 31.5 GBTTFT: fast
ollama run gemma4:e4b
154
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~124 ms (fast)
Model details →Run-on benchmark page →
#7Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 31.5 GBTTFT: fast
ollama run gemma3:4b
154
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~124 ms (fast)
Model details →Run-on benchmark page →
#8CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 30.1 GBTTFT: fast
ollama run codegemma:7b
155
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~217 ms (fast)
Model details →Run-on benchmark page →
#9PaliGemma 2 3B
3B
gemma
Commercial OK
Quant: BF16Context: 8,192VRAM: 17.8 GBHeadroom: 30.2 GBTTFT: instant
109
tok/s
E
Weights
6.00 GB
KV cache
1.50 GB
Activations
8.49 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~93 ms (instant)
Model details →Run-on benchmark page →
#10Dolphin 3.0 Mistral 24B
24B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 37.2 GBHeadroom: 10.8 GBTTFT: noticeable
ollama run dolphin-mistral:24b
45
tok/s
E
Weights
14.49 GB
KV cache
12.00 GB
Activations
8.92 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~744 ms (noticeable)
Model details →Run-on benchmark page →
#11Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 33.2 GBTTFT: instant
ollama run llama3.2:3b
206
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): ~93 ms (instant)
Model details →Run-on benchmark page →
#12Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.8 GBHeadroom: 33.2 GBTTFT: fast
258
tok/s
E
Weights
2.54 GB
KV cache
2.10 GB
Activations
8.32 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~130 ms (fast)
Model details →Run-on benchmark page →

Runs with tradeoffs
22 models

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

Hermes 3 Llama 3.1 70B
70B
hermes
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 57.0 GBHeadroom: 10.2 GBTTFT: slow
  • • Partial CPU offload: ~16% of layers run on CPU
ollama run hermes3:70b
16
tok/s
E
Weights
42.26 GB
KV cache
8.75 GB
Activations
4.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~2169 ms (slow)
Model details →Run-on benchmark page →
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 45.1 GBHeadroom: 2.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
ollama run gemma4:31b
35
tok/s
E
Weights
18.72 GB
KV cache
15.50 GB
Activations
9.13 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~961 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 44.0 GBHeadroom: 4.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
ollama run qwen3:30b
36
tok/s
E
Weights
18.11 GB
KV cache
15.00 GB
Activations
9.10 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~930 ms (noticeable)
Model details →Run-on benchmark page →
Nemotron 3 Nano (30B-A3B)
30B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 44.0 GBHeadroom: 4.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
ollama run nemotron3:nano
36
tok/s
E
Weights
18.11 GB
KV cache
15.00 GB
Activations
9.10 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~930 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 46.3 GBHeadroom: 1.7 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run qwen3:32b
34
tok/s
E
Weights
19.32 GB
KV cache
16.00 GB
Activations
9.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~992 ms (noticeable)
Model details →Run-on benchmark page →
DeepSeek R1 Distill Qwen 32B
32B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 46.3 GBHeadroom: 1.7 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run deepseek-r1:32b
34
tok/s
E
Weights
19.32 GB
KV cache
16.00 GB
Activations
9.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~992 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 2.5 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 46.3 GBHeadroom: 1.7 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run qwen2.5:32b
34
tok/s
E
Weights
19.32 GB
KV cache
16.00 GB
Activations
9.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~992 ms (noticeable)
Model details →Run-on benchmark page →
QwQ 32B Preview
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 46.3 GBHeadroom: 1.7 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run qwq:32b
34
tok/s
E
Weights
19.32 GB
KV cache
16.00 GB
Activations
9.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~992 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: 7 new comfortable, 28 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Whisper Large v3 Turbo
  • • SmolLM 2 360M Instruct
Shop this upgrade↗

Upgrade to NVIDIA H100 SXM

see current pricing

80 GB VRAM (vs your 48 GB) plus a bandwidth jump from ~1008 GB/s to ~3350 GB/s.

Unlocks: 30 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • QwQ 32B Preview
  • • OLMo 2 32B
Shop this upgrade↗

Add a second NVIDIA RTX 4090 48GB (China-mod)

~$2400

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: 36 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • QwQ 32B Preview
  • • OLMo 2 32B
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 (48 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 (48 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 (48 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 (48 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 (48 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.