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

143 models fit comfortably on Razer Blade 16 (2025, RTX 5090 Mobile) with 64 GB system RAM. 73 more run with tradeoffs. Top pick: CodeGemma 7B.

What's the recommended runner for this hardware?

llama.cpp / Ollama (CUDA). This is the runner that gives best tokens-per-second on Razer Blade 16 (2025, RTX 5090 Mobile) based on windows support and the GPU backend.

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

Build: Razer Blade 16 (2025, RTX 5090 Mobile) + — + 64 GB RAM (windows)

Memory: 24 GB VRAM + 64 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
143 models

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

#1CodeGemma 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

4.20 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: 19.8 GBHeadroom: 4.2 GB

ollama run deepseek-coder-v2:16b

tok/s

Estimated

Weights

9.50 GB

KV cache

8.00 GB

Activations

0.48 GB

Runtime

1.80 GB

Model details →

#3Codestral 22B

22B

mistral

Quant: Q4_K_MContext: 2,048VRAM: 18.2 GBHeadroom: 5.8 GB

ollama run codestral:22b

tok/s

Estimated

Weights

13.00 GB

KV cache

2.75 GB

Activations

0.65 GB

Runtime

1.80 GB

Model details →

#4Qwen 3 14B

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GB

ollama run qwen3:14b

tok/s

Estimated

Weights

8.40 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: 19.8 GBHeadroom: 4.2 GB

ollama run qwen2.5:14b

tok/s

Estimated

Weights

10.50 GB

KV cache

7.00 GB

Activations

0.53 GB

Runtime

1.80 GB

Model details →

#6Qwen 2.5 7B Instruct

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GB

ollama run qwen2.5:7b

tok/s

Estimated

Weights

8.10 GB

KV cache

0.47 GB

Activations

0.41 GB

Runtime

1.80 GB

Model details →

#7Qwen 3 8B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GB

ollama run qwen3:8b

tok/s

Estimated

Weights

8.20 GB

KV cache

4.00 GB

Activations

0.42 GB

Runtime

1.80 GB

Model details →

#8Mistral Small 3 24B

24B

mistral

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 19.5 GBHeadroom: 4.5 GB

ollama run mistral-small:24b

tok/s

Estimated

Weights

14.00 GB

KV cache

3.00 GB

Activations

0.70 GB

Runtime

1.80 GB

Model details →

#9Llama 3.1 8B Instruct

llama

Commercial OK

Quant: FP16Context: 8,192VRAM: 19.8 GBHeadroom: 4.2 GB

ollama run llama3.1:8b

tok/s

Estimated

Weights

16.10 GB

KV cache

1.07 GB

Activations

0.81 GB

Runtime

1.80 GB

Model details →

#10Gervásio 8B PTPT

llama

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 8.4 GBHeadroom: 15.6 GB

128

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.22 GB

Runtime

1.80 GB

Model details →

#11StarCoder 2 7B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 14.1 GB

146

tok/s

Estimated

Weights

4.40 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

1.80 GB

Model details →

#12StarCoder 2 3B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.4 GBHeadroom: 18.6 GB

341

tok/s

Estimated

Weights

2.00 GB

KV cache

1.50 GB

Activations

0.11 GB

Runtime

1.80 GB

Model details →

Runs with tradeoffs
73 models

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

Qwen 2.5 Coder 32B Instruct

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 0.1 GB

• Tight VRAM fit — only 0.1 GB headroom left for context growth

ollama run qwen2.5-coder:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

2.15 GB

Activations

0.96 GB

Runtime

1.80 GB

Model details →

Muse Glimmer 30B

30B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 20.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run muse-glimmer

tok/s

Estimated

Weights

17.00 GB

KV cache

0.71 GB

Activations

0.86 GB

Runtime

1.80 GB

Model details →

Qwen 3 30B-A3B

30B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 50.4 GBHeadroom: 12.0 GB

• Partial CPU offload: ~52% of layers run on CPU

ollama run qwen3:30b

tok/s

Estimated

Weights

32.00 GB

KV cache

15.00 GB

Activations

1.61 GB

Runtime

1.80 GB

Model details →

Gemma 4 31B Dense

31B

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 52.0 GBHeadroom: 10.4 GB

• Partial CPU offload: ~54% of layers run on CPU

ollama run gemma4:31b

tok/s

Estimated

Weights

33.00 GB

KV cache

15.50 GB

Activations

1.66 GB

Runtime

1.80 GB

Model details →

Qwen 3 32B

32B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 53.5 GBHeadroom: 8.9 GB

• Partial CPU offload: ~55% of layers run on CPU

ollama run qwen3:32b

tok/s

Estimated

Weights

34.00 GB

KV cache

16.00 GB

Activations

1.71 GB

Runtime

1.80 GB

Model details →

Qwen 2.5 32B Instruct

32B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 53.5 GBHeadroom: 8.9 GB

• Partial CPU offload: ~55% of layers run on CPU

ollama run qwen2.5:32b

tok/s

Estimated

Weights

34.00 GB

KV cache

16.00 GB

Activations

1.71 GB

Runtime

1.80 GB

Model details →

Llama 3.1 70B Instruct

70B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 52.6 GBHeadroom: 9.8 GB

• Partial CPU offload: ~54% of layers run on CPU

ollama run llama3.1:70b

tok/s

Estimated

Weights

40.00 GB

KV cache

8.75 GB

Activations

2.00 GB

Runtime

1.80 GB

Model details →

Llama 3.3 70B Instruct

70B

llama

Commercial OK

Quant: Q5_K_MContext: 8,192VRAM: 53.8 GBHeadroom: 8.6 GB

• Partial CPU offload: ~55% of layers run on CPU

ollama run llama3.3:70b

tok/s

Estimated

Weights

47.00 GB

KV cache

2.68 GB

Activations

2.36 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: 41 new comfortable, 79 new tradeoff

• Qwen 3 0.6B
• Qwen 3 1.7B
• Gemma 3 270M
• SmolLM2 135M Instruct

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: 80 new comfortable

• Qwen 3 0.6B
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen3.6 27B

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: 93 new comfortable

• Qwen 3 0.6B
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen3.6 27B

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