What can Razer Blade 16 (2025, RTX 5090 Mobile) run for chat?
Build: Razer Blade 16 (2025, RTX 5090 Mobile 24GB)
Runs comfortably175 models
Ranked by fit for chat use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 21.6 GB1705tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 17.1 GBollama run llama3.2:3b194tok/sEstimated
ollama run llama3.2:3bQuant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 20.4 GB602tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GBollama run dolphin3:8b128tok/sEstimated
ollama run dolphin3:8bQuant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 21.4 GB930tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 20.0 GB511tok/sEstimated
Quant: Q5_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 13.3 GBollama run mistral:7b128tok/sEstimated
ollama run mistral:7bQuant: Q4_K_MContext: 8,192VRAM: 18.7 GBHeadroom: 5.3 GB426tok/sEstimated
Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 16.4 GB146tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 19.4 GBollama run alibayram/kumru:latest426tok/sEstimated
ollama run alibayram/kumru:latestQuant: Q4_K_MContext: 8,192VRAM: 9.4 GBHeadroom: 14.6 GB146tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 21.4 GB930tok/sEstimated
Runs with tradeoffs73 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 23.4 GBHeadroom: 0.6 GB- • Tight VRAM fit — only 0.6 GB headroom left for context growth
51tok/sEstimated
- • Tight VRAM fit — only 0.6 GB headroom left for context growth
Quant: Q4_K_MContext: 4,096VRAM: 21.7 GBHeadroom: 2.3 GB- • Tight VRAM fit — only 2.3 GB headroom left for context growth
43tok/sEstimated
- • Tight VRAM fit — only 2.3 GB headroom left for context growth
Quant: 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: 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: 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: 29.9 GBHeadroom: 32.5 GB- • Partial CPU offload: ~20% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
5tok/sEstimated
- • Partial CPU offload: ~20% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
Quant: 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: 9 new comfortable, 79 new tradeoff
- • Gemma 3 270M
- • SmolLM2 135M Instruct
- • SmolLM2 360M Instruct
- • VBART Large (Turkish Summarization)
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: 48 new comfortable
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
- • Qwen 3 32B
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: 61 new comfortable
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen3.6 27B
- • Qwen 3 32B
Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.
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
Want a specific benchmark we don't have? Email Contact support and we'll prioritize it.