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

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

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
208 models

Full-VRAM resident, with room for context. No compromises.

#1all-MiniLM-L6-v2
0.022B
other
Commercial OK
Quant: Q4_K_MContext: 256VRAM: 1.8 GBHeadroom: 22.2 GB
46490
tok/s
Estimated
Weights
0.01 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#2Piper
0.025B
other
Commercial OK
Quant: Q4_K_MContext: 0VRAM: 1.8 GBHeadroom: 22.2 GB
40911
tok/s
Estimated
Weights
0.02 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#3Whisper Tiny
0.039B
other
Commercial OK
Quant: Q4_K_MContext: 30VRAM: 1.8 GBHeadroom: 22.2 GB
26225
tok/s
Estimated
Weights
0.02 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#4Whisper Base
0.074B
other
Commercial OK
Quant: Q4_K_MContext: 30VRAM: 1.8 GBHeadroom: 22.2 GB
13821
tok/s
Estimated
Weights
0.04 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#5Kokoro 82M
0.082B
other
Commercial OK
Quant: Q4_K_MContext: 0VRAM: 1.9 GBHeadroom: 22.1 GB
12473
tok/s
Estimated
Weights
0.05 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#6all-mpnet-base-v2
0.109B
other
Commercial OK
Quant: Q4_K_MContext: 384VRAM: 1.9 GBHeadroom: 22.1 GB
9383
tok/s
Estimated
Weights
0.07 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#7paraphrase-multilingual-MiniLM-L12-v2
0.118B
other
Commercial OK
Quant: Q4_K_MContext: 128VRAM: 1.9 GBHeadroom: 22.1 GB
8668
tok/s
Estimated
Weights
0.07 GB
KV cache
0.00 GB
Activations
0.00 GB
Runtime
1.80 GB
Model details →
#8Nomic Embed Text v1.5
0.137B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 22.0 GB
7466
tok/s
Estimated
Weights
0.08 GB
KV cache
0.07 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#9SmolLM2 135M Instruct
0.135B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 22.0 GB
7576
tok/s
Estimated
Weights
0.08 GB
KV cache
0.07 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#10GTE ModernBERT Base
0.149B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.0 GBHeadroom: 22.0 GB
6864
tok/s
Estimated
Weights
0.09 GB
KV cache
0.07 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#11Whisper Small
0.244B
other
Commercial OK
Quant: Q4_K_MContext: 30VRAM: 2.0 GBHeadroom: 22.0 GB
4192
tok/s
Estimated
Weights
0.15 GB
KV cache
0.00 GB
Activations
0.01 GB
Runtime
1.80 GB
Model details →
#12Gemma 3 270M
0.27B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 21.9 GB
3788
tok/s
Estimated
Weights
0.16 GB
KV cache
0.14 GB
Activations
0.02 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
52 models

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

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 →
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 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 →
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 →
DeepSeek R1 Distill Qwen 32B
32B
deepseek
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 deepseek-r1:32b
32
tok/s
Estimated
Weights
19.32 GB
KV cache
16.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Gemma 4 26B MoE
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 21.5 GBHeadroom: 2.5 GB
  • • Tight VRAM fit — only 2.5 GB headroom left for context growth
ollama run gemma4:26b-moe
39
tok/s
Estimated
Weights
15.70 GB
KV cache
3.25 GB
Activations
0.79 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 →
Nemotron 3 Nano (30B-A3B)
30B
other
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 nemotron3:nano
34
tok/s
Estimated
Weights
18.11 GB
KV cache
15.00 GB
Activations
0.91 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: 65 new tradeoff

  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Llama 3.3 70B Instruct
  • • Qwen 3 32B
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: 26 new comfortable

  • • Mistral Nemo 12B Instruct
  • • Gemma 3 12B
  • • Pixtral 12B
  • • Gemma 3 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: 50 new comfortable

  • • Mistral Nemo 12B Instruct
  • • Gemma 3 12B
  • • Pixtral 12B
  • • Gemma 4 26B MoE
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.

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