What can Razer Blade 16 (2025, RTX 5090 Mobile) run for long context?
Build: Razer Blade 16 (2025, RTX 5090 Mobile 24GB)
Runs comfortably143 models
Ranked by fit for long context use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBollama run gemma4:e4b145tok/sEstimated
ollama run gemma4:e4bQuant: Q8_0Context: 8,192VRAM: 8.0 GBHeadroom: 16.0 GBollama run phi3.5:3.8b153tok/sEstimated
ollama run phi3.5:3.8bQuant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 13.6 GB128tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB146tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB146tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.3 GBHeadroom: 18.7 GB341tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GB128tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GBollama run qwen2.5:7b83tok/sEstimated
ollama run qwen2.5:7bQuant: Q5_K_MContext: 8,192VRAM: 16.9 GBHeadroom: 7.1 GBollama run mistral-nemo:12b75tok/sEstimated
ollama run mistral-nemo:12bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run qwen3:14b73tok/sEstimated
ollama run qwen3:14bQuant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GBollama run qwen3:8b73tok/sEstimated
ollama run qwen3:8bQuant: Q5_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run qwen2.5:14b64tok/sEstimated
ollama run qwen2.5:14bRuns with tradeoffs73 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 20.1 GBHeadroom: 3.9 GB- • Tight VRAM fit — only 3.9 GB headroom left for context growth
39tok/sEstimated
- • Tight VRAM fit — only 3.9 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB- • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27b38tok/sEstimated
- • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27bQuant: 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-glimmer34tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run muse-glimmerQuant: Q4_K_MContext: 8,192VRAM: 59.3 GBHeadroom: 3.1 GB- • Partial CPU offload: ~60% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
6tok/sEstimated
- • Partial CPU offload: ~60% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: Q8_0Context: 8,192VRAM: 50.4 GBHeadroom: 12.0 GB- • Partial CPU offload: ~52% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run nemotron3:nano2tok/sEstimated
- • Partial CPU offload: ~52% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run nemotron3:nanoQuant: Q8_0Context: 8,192VRAM: 20.4 GBHeadroom: 3.6 GB- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11b53tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11bQuant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moe39tok/sEstimated
- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moeQuant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-a4b-it-q4_K_M39tok/sEstimated
- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-a4b-it-q4_K_MWhat 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
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
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
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Won't runtop 5 popular models
Need more memory than you have. Shown for orientation.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.
How to read these numbers
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