RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
RUNLOCALAI

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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
← Back to Will-it-run

Custom build engine

Describe your build — any GPUs, CPU, RAM, OS, runtime, use case. We'll compute effective VRAM honestly, recommend a runtime, and tell you which models fit comfortably, which are borderline, and which aren't practical.

Total VRAM ≠ pooled VRAM. We never sum VRAM unless the silicon truly pools (Apple unified memory). We always explain why effective is lower than total.

Describe your build

Add GPUs, set CPU/RAM/OS, optionally pick a runtime + use case. URL updates as you change fields — share a build by copying the URL.

Build summary

Total VRAM
24 GB
Effective VRAM
~22 GB
range 20-22 GB
Topology
single gpu
none
Setup difficulty
beginner
speed penalty ~0%
Why effective VRAM is lower than total

Single AMD Radeon RX 7900 XTX — 24 GB VRAM minus ~1.8 GB runtime overhead = ~22 GB usable for weights + KV cache + activations. The 8% headroom we reserve covers the typical OS/driver footprint and gives KV-cache room for an 8K-32K context.

Recommended runtime

Best engine for this topology + skill level + use case.

llama.cpp (HIPBLAS)
primary
moderate

The most reliable AMD inference path in 2026. GGUF format works on every AMD card; HIPBLAS backend matches llama.cpp's CUDA backend within ~20% on RDNA3.

Models that fit your build

183 models considered. Categorized by headroom at the recommended quant + a sensible context for your use case.

Comfortable
24 models · ≥15% headroom
ModelParamsQuantVRAM est.ContextNote
OLMo 2 13B13BQ4_K_M17.6 GB4,096Fits cleanly at Q4_K_M + 4,096 ctx with 20% headroom.
Stable LM 2 12B12BQ4_K_M16.7 GB4,096Fits cleanly at Q4_K_M + 4,096 ctx with 24% headroom.
Molmo 7B-D8BQ4_K_M13.2 GB4,096Comfortable fit with 40% headroom — room to extend context or run alongside other workloads.
Granite 3.0 8B Instruct8BQ4_K_M13.2 GB4,096Comfortable fit with 40% headroom — room to extend context or run alongside other workloads.
CodeGemma 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Falcon Mamba 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Qwen 2.5 7B Instruct7BQ8_018.5 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 16% headroom.
Qwen 2.5-VL 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Codestral Mamba 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Qwen 3 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Qwen 2.5 Math 7B7BQ4_K_M12.3 GB4,096Comfortable fit with 44% headroom — room to extend context or run alongside other workloads.
Janus-Pro 7B7BQ4_K_M12.3 GB4,096Comfortable fit with 44% headroom — room to extend context or run alongside other workloads.
Qwen 2-VL 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
StarCoder 2 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
CodeQwen 1.5 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
LLaVA 1.6 Mistral 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
LLaVA-OneVision 7B7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Falcon 3 7B Instruct7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
InternLM 2.5 7B Chat7BQ4_K_M18.1 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 18% headroom.
Phi-3.5 Vision4BQ4_K_M15 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 32% headroom.
Qwen 3 4B4BQ8_016.7 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 24% headroom.
Gemma 4 E4B (Effective 4B)4BQ8_016.7 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 24% headroom.
Gemma 3 4B4BQ8_016.7 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 24% headroom.
MiniCPM 3 4B4BQ4_K_M14.7 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 33% headroom.
Borderline
16 models · tight, may need quant downgrade
ModelParamsQuantVRAM est.ContextNote
DeepSeek MoE 16B Base16BQ4_K_M20.2 GB4,096Tight fit at Q4_K_M — only 8% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Falcon 3 10B10BQ4_K_M21.5 GB8,192Tight fit at Q4_K_M — only 2% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Gemma 2 9B Instruct9BQ4_K_M20.4 GB8,192Tight fit at Q4_K_M — only 7% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Yi Coder 9B9BQ4_K_M20.4 GB8,192Tight fit at Q4_K_M — only 7% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Nemotron 3 Nano 9B9BQ4_K_M20.4 GB8,192Tight fit at Q4_K_M — only 7% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
GLM-4 9B9BQ4_K_M20.4 GB8,192Tight fit at Q4_K_M — only 7% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Tulu 3 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Llama 3.1 Nemotron Nano 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
DeepSeek R1 Distill Llama 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
MiniCPM-V 2.6 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
OpenCoder 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Granite 3.2 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
InternLM 3 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Granite 3.3 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Ministral 8B Instruct8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
MiniCPM-V 3 8B8BQ4_K_M19.3 GB8,192Tight fit at Q4_K_M — only 12% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Not practical
16 models · oversize for this build
ModelParamsQuantVRAM est.ContextNote
NV-Embed v28BFP1630.6 GB8,192~30.6 GB needed at FP16 + 8,192 ctx — overshoots effective VRAM by 39%. Drop quant or move to a larger build.
Qwen 3 Embedding 8B8BFP1631 GB8,192~31.0 GB needed at FP16 + 8,192 ctx — overshoots effective VRAM by 41%. Drop quant or move to a larger build.
PaliGemma 2 10B10BBF1636.2 GB8,192~36.2 GB needed at BF16 + 8,192 ctx — overshoots effective VRAM by 65%. Drop quant or move to a larger build.
Llama 3.2 11B Vision Instruct11BQ4_K_M22.7 GB8,192~22.7 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 3%. Drop quant or move to a larger build.
Llama 3.2 11B Vision11BQ4_K_M22.7 GB8,192~22.7 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 3%. Drop quant or move to a larger build.
Pixtral 12B12BQ4_K_M23.8 GB8,192~23.8 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 8%. Drop quant or move to a larger build.
Gemma 3 12B12BQ4_K_M23.8 GB8,192~23.8 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 8%. Drop quant or move to a larger build.
Mistral Nemo 12B Instruct12BQ4_K_M23.8 GB8,192~23.8 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 8%. Drop quant or move to a larger build.
Baichuan 4 13B13BQ4_K_M24.9 GB8,192~24.9 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 13%. Drop quant or move to a larger build.
GLM-4V 9B14BQ4_K_M26 GB8,192~26.0 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
DeepSeek R1 Distill Qwen 14B14BQ4_K_M26.1 GB8,192~26.1 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Qwen 2.5 14B Instruct14BQ4_K_M26.1 GB8,192~26.1 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Phi-4 Multimodal14BQ4_K_M26.1 GB8,192~26.1 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Qwen 2.5 Coder 14B Instruct14BQ4_K_M26.1 GB8,192~26.1 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Phi-4 14B14BQ4_K_M26.1 GB8,192~26.1 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Phi-4 Reasoning 14B14BQ4_K_M26.1 GB8,192~26.1 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.

Related

Multi-GPU buying guide →

NVLink vs PCIe, tensor- vs pipeline-parallel, mixed-card honesty.

Hardware combinations →

Curated multi-GPU / cluster setups with effective-VRAM math.

Setup path-finder →

OS + runtime install commands for your stack.

Compatibility matrix →

Runtime × OS × hardware support truth table.

Shopping a full build instead of a single card?

If you're sizing a fresh AI build (not just a card to drop into an existing system), the build-budget walkthroughs cover the whole BOM honestly: AI PC build under $1,000 or AI PC build under $2,000 cover the realistic 2026 budget tiers.

Vertical-fit shopping? AI PC for students covers the budget + portability tradeoffs; AI PC for developers covers the coding workflow specifics; AI PC for small business covers the document-RAG / always-on machine.

Form-factor first? See best laptop for local AI, best Mac for local AI, best mini PC for local AI, or best used GPU for local AI.

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