RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / Razer Blade 16 (2025, RTX 5090 Mobile) / coding

What can Razer Blade 16 (2025, RTX 5090 Mobile) run for coding?

Build: Razer Blade 16 (2025, RTX 5090 Mobile) + — + 32 GB RAM (windows)

Memory: 24 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
135 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: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB
ollama run codegemma:7b
146
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#2DeepSeek Coder V2 Lite (16B)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 20.0 GBHeadroom: 4.0 GB
ollama run deepseek-coder-v2:16b
64
tok/s
Estimated
Weights
9.66 GB
KV cache
8.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
#3Codestral 22B
22B
mistral
Quant: Q4_K_MContext: 2,048VRAM: 18.5 GBHeadroom: 5.5 GB
ollama run codestral:22b
46
tok/s
Estimated
Weights
13.28 GB
KV cache
2.75 GB
Activations
0.67 GB
Runtime
1.80 GB
Model details →
#4Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.7 GBHeadroom: 6.3 GB
ollama run qwen3:14b
73
tok/s
Estimated
Weights
8.45 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#5Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 18.9 GBHeadroom: 5.1 GB
ollama run qwen2.5:14b
64
tok/s
Estimated
Weights
9.63 GB
KV cache
7.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
#6Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.1 GBHeadroom: 13.9 GB
ollama run qwen2.5:7b
83
tok/s
Estimated
Weights
7.44 GB
KV cache
0.47 GB
Activations
0.38 GB
Runtime
1.80 GB
Model details →
#7Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.7 GBHeadroom: 9.3 GB
ollama run qwen3:8b
73
tok/s
Estimated
Weights
8.50 GB
KV cache
4.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#8Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: FP16Context: 8,192VRAM: 19.7 GBHeadroom: 4.3 GB
ollama run llama3.1:8b
39
tok/s
Estimated
Weights
16.00 GB
KV cache
1.07 GB
Activations
0.81 GB
Runtime
1.80 GB
Model details →
#9Gervásio 8B PTPT
8B
llama
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 8.9 GBHeadroom: 15.1 GB
128
tok/s
Estimated
Weights
4.83 GB
KV cache
2.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#10StarCoder 2 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB
146
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#11StarCoder 2 3B
3B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 18.8 GB
341
tok/s
Estimated
Weights
1.81 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Model details →
#12OpenCoder 8B
8B
opencoder
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 13.1 GB
128
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
52 models

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

Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.6 GBHeadroom: 1.4 GB
  • • Tight VRAM fit — only 1.4 GB headroom left for context growth
ollama run qwen2.5-coder:32b
32
tok/s
Estimated
Weights
19.32 GB
KV cache
0.54 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 35.8 GBHeadroom: 7.4 GB
  • • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3:30b
34
tok/s
Estimated
Weights
18.11 GB
KV cache
15.00 GB
Activations
0.91 GB
Runtime
1.80 GB
Model details →
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 37.0 GBHeadroom: 6.2 GB
  • • Partial CPU offload: ~35% of layers run on CPU
ollama run gemma4:31b
33
tok/s
Estimated
Weights
18.72 GB
KV cache
15.50 GB
Activations
0.94 GB
Runtime
1.80 GB
Model details →
Qwen 2.5 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 38.1 GBHeadroom: 5.1 GB
  • • Partial CPU offload: ~37% of layers run on CPU
ollama run qwen2.5:32b
32
tok/s
Estimated
Weights
19.32 GB
KV cache
16.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 40.9 GBHeadroom: 2.3 GB
  • • Partial CPU offload: ~41% of layers run on CPU
ollama run qwen3:32b
28
tok/s
Estimated
Weights
22.00 GB
KV cache
16.00 GB
Activations
1.11 GB
Runtime
1.80 GB
Model details →
Mistral Small 3 24B
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 20.0 GBHeadroom: 4.0 GB
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
ollama run mistral-small:24b
43
tok/s
Estimated
Weights
14.49 GB
KV cache
3.00 GB
Activations
0.73 GB
Runtime
1.80 GB
Model details →
Qwen 3 Coder 32B
32B
qwen
Commercial OK
Quant: AWQ-INT4Context: 2,048VRAM: 39.4 GBHeadroom: 3.8 GB
  • • Partial CPU offload: ~39% of layers run on CPU
19
tok/s
Estimated
Weights
32.00 GB
KV cache
4.00 GB
Activations
1.60 GB
Runtime
1.80 GB
Model details →
DeepSeek Coder V3
33B
deepseek
Commercial OK
Quant: AWQ-INT4Context: 2,048VRAM: 40.6 GBHeadroom: 2.6 GB
  • • Partial CPU offload: ~41% of layers run on CPU
19
tok/s
Estimated
Weights
33.00 GB
KV cache
4.13 GB
Activations
1.65 GB
Runtime
1.80 GB
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: 73 new comfortable, 65 new tradeoff

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Upgrade to NVIDIA RTX PRO 4500 Blackwell

see current pricing

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~? GB/s to ~896 GB/s.

Unlocks: 99 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Add a second Razer Blade 16 (2025, RTX 5090 Mobile)

see current pricing

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: 123 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
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 (24 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 (24 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 (24 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 (24 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 (24 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 →

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