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AI Model Radar

Hardware guide

Best local models for
RTX 5090

◇ Estimated— Estimated: computed from our curated model and hardware catalog — not a live reading.

Specifications

Memory
32 GB
Memory type
Dedicated VRAM
Bandwidth
1792 GB/s
Class
Top consumer

The short answer

Qwen AgentWorld 35B-A3B

Weights ~20.6 GB (Q4_K_M), context ~0.2 GB at 8K tokens, runtime ~1.5 GB. Your 32 GB leaves ~28.2 GB usable after a 12% safety margin.

GGUF (Q4_K_M) ↗ (External link)Original (safetensors) ↗ (External link)

Fit labels

  • Excellent fit
  • Good fit
  • Tight fit
  • Offload required
  • Not recommended

Every curated model on this card

Context length: 8K tokens · f16 (default)

Model fit on the selected hardware
ModelFitEst. memoryEstimated memory = weights + context + runtime
Qwen3-Coder 30B-A3BAlibaba Qwen · 30.5B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~19.4 GB17.3 + 0.8 + 1.4
GLM-4.7-FlashZhipu AI · 31.2B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Excellent fit~18.9 GB17.1 + 0.4 + 1.4
Gemma 4 26B-A4BGoogle · 25.8B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~17.6 GB15.8 + 0.5 + 1.3
Gemma 3 27BGoogle · 27.4B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~17.7 GB15.4 + 1.0 + 1.3
Qwen3.8 27BAlibaba Qwen · 27.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~17.1 GB15.3 + 0.5 + 1.3
Mistral Small 3.2Mistral AI · 24B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~15.8 GB13.3 + 1.3 + 1.2
Devstral Small 2 24BMistral AI · 24B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~15.8 GB13.3 + 1.3 + 1.2
gpt-oss 20BOpenAI · 20.9B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~12.2 GB10.8 + 0.2 + 1.2
Qwen3 14BAlibaba Qwen · 14.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~10.8 GB8.4 + 1.3 + 1.1
Gemma 4 12BGoogle · 12B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~8.5 GB6.6 + 0.8 + 1.0
Nemotron Nano 9B v2NVIDIA · 8.9B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Excellent fit~8.9 GB6.1 + 1.8 + 1.0
Ornith 1.5 9BOrnith AI · 9.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~6.7 GB5.4 + 0.3 + 1.0
Granite 4.2 8BIBM · 8.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~7.3 GB5.0 + 1.3 + 1.0
LFM2.5 8B-A1BLiquid AI · 8.5B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~5.9 GB4.8 + 0.1 + 1.0
Qwen3 8BAlibaba Qwen · 8.2B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~6.8 GB4.7 + 1.1 + 1.0
Gemma 4 E4BGoogle · 8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~5.7 GB4.6 + 0.1 + 1.0
Ling 3.0 TinyInclusionAI · 7.9B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Excellent fit~5.7 GB4.5 + 0.2 + 1.0
Qwen3 4BAlibaba Qwen · 4B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~4.3 GB2.3 + 1.1 + 0.9
Phi-4 MiniMicrosoft · 3.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~4.2 GB2.3 + 1.0 + 0.9
Granite 4.2 3BIBM · 3.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~3.6 GB2.1 + 0.6 + 0.9
LFM2.5 2.6BLiquid AI · 2.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~2.6 GB1.6 + 0.1 + 0.9
LFM2.5 1.2BLiquid AI · 1.2B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~1.7 GB0.7 + 0.1 + 0.9
Qwen3 0.6BAlibaba Qwen · 0.6B · Q8_0GGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Excellent fit~2.3 GB0.6 + 0.9 + 0.9
Qwen AgentWorld 35B-A3BAlibaba Qwen · 34.7B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Good fit~22.2 GB20.6 + 0.2 + 1.5
Ornith 1.5 35B-A3BOrnith AI · 36B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Good fit~21.8 GB20.2 + 0.2 + 1.5
KAT-Coder V2.5Kwaipilot · 34.7B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Good fit~21.5 GB19.9 + 0.2 + 1.4
Qwen3 32BAlibaba Qwen · 32.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Good fit~21.8 GB18.4 + 2.0 + 1.4
Gemma 4 31BGoogle · 31.3B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Good fit~20.5 GB17.1 + 2.0 + 1.4
Granite 4.2 30BIBM · 29.3B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Good fit~19.8 GB16.5 + 2.0 + 1.3
Ling 3.0 FlashInclusionAI · 127B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Offload required~73.4 GB70.1 + 0.4 + 3.0
GLM-4.5-AirZhipu AI · 110B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Offload required~72.3 GB68.0 + 1.4 + 2.9
gpt-oss 120BOpenAI · 117B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Offload required~61.4 GB58.5 + 0.3 + 2.6
Qwen3-Coder NextAlibaba Qwen · 79.7B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Offload required~48.1 GB45.1 + 0.8 + 2.2
Llama 3.3 70BMeta · 70.6B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Offload required~44.1 GB39.6 + 2.5 + 2.0
Qwen3.8 2.4T-A95BAlibaba Qwen · 2.4T-A95B · IQ4_XSGGUF ↗ (External link)Original ↗ (External link)Not recommended~1259.0 GB1220.8 + 0.7 + 37.5
DeepSeek-V4-ProDeepSeek · 1650B MoE · Q4_KGGUF ↗ (External link)Original ↗ (External link)Not recommended~816.4 GB791.3 + 0.5 + 24.6
GLM-5.3Zhipu AI · 753B · Q4_KGGUF ↗ (External link)Original ↗ (External link)Not recommended~449.8 GB435.2 + 0.7 + 13.9
GLM-5.2Zhipu AI · 753B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Not recommended~448.3 GB433.8 + 0.7 + 13.9
Ornith 1.5 397BOrnith AI · 403B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Not recommended~235.4 GB227.5 + 0.2 + 7.7
GLM-5.3-FlashZhipu AI · 321B · Q4_KGGUF ↗ (External link)Original ↗ (External link)Not recommended~192.5 GB186.0 + 0.1 + 6.4
Hy3Tencent · 299B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Not recommended~174.6 GB166.3 + 2.5 + 5.8
DeepSeek-V4-FlashDeepSeek · 304B MoE · Q4_KGGUF ↗ (External link)Original ↗ (External link)Not recommended~150.0 GB144.4 + 0.4 + 5.2
Qwen3.8 Flash-NextAlibaba Qwen · 180B MoE · Q4_KGGUF ↗ (External link)Original ↗ (External link)Not recommended~107.8 GB103.7 + 0.2 + 4.0
Llama 3.1 8BMeta · 8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Unknown—
Llama 3.2 3BMeta · 3B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Unknown—

All memory figures are estimates: measured quantized file size + computed context memory + runtime overhead, with a 12% safety margin on your hardware. Real usage varies with runtime version and settings.

GGUF is the quantized single-file format local runtimes load (Ollama, LM Studio, llama.cpp); our memory figures are measured from the linked GGUF file. The original repo holds full-precision safetensors — a much larger download meant for GPUs with far more memory.

Same memory, different speed

No other card in our catalogue carries this much memory, so nothing here is a like-for-like alternative.

When this card is not enough

Own the middle.

DGX Spark and GB10-class systems put 128 GB of unified memory on your desk. Rational when large local models are your daily routine.

Read our DGX Spark profile →

Path C — Elastic compute

Rent the spike.

Occasional heavy job? Rent an H100 for the afternoon instead of buying hardware that idles the rest of the year.

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