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.

Calculations follow the RunLocalAI Will-It-Run Framework: effective VRAM, model working set, runtime constraints, fit tiers, and measured-vs-estimated evidence labels.

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.

GPUs in your build

CPU class

System RAM (GB)

Use case

Your skill level

Runtime preference

Interconnect (force override)

Split strategy (force override)

Multi-node deployment

Allow CPU offload as fallback

Build summary

Total VRAM

48 GB

Effective VRAM

~33 GB

range 28-36 GB

Topology

mixed gpu

pcie

Setup difficulty

advanced

speed penalty ~35%

Why effective VRAM is lower than total

Mixed-GPU (asymmetric) configuration. Tensor-parallel doesn't work cleanly because TP requires identical cards — your faster card stalls waiting on the slower one every layer. Use llama.cpp's layer-split with manual --tensor-split tuning to distribute layers by VRAM ratio. Effective capacity ~33 GB after layer-split overhead, but the slowest card (22 GB effective) bottlenecks single-tensor operations.

Measured evidence on this hardware

Publicly inspectable measured rows for the selected hardware slug(s). Exact measured rows calibrate the fit table instead of leaving it as pure VRAM estimation.

No publicly inspectable benchmark rows are attached to this exact hardware yet. The engine will still calculate fit and runtime, but speed rows will remain estimated.

Recommended runtime

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

llama.cpp (layer-split)

primary

involved

Mixed-GPU configurations need llama.cpp's --tensor-split flag with manual ratio tuning by VRAM. vLLM's tensor-parallel requires identical cards and won't run cleanly here.

Ollama

alternative

moderate

Inherits llama.cpp's layer-split path with friendlier UX. OLLAMA_GPU_OVERHEAD and per-card env vars do most of what manual flags do.

WORKLOAD PROFILE

FITS

Pollux Judge 32B @ Q4_K_M, 4K context on NVIDIA GeForce RTX 4090

0 GB33 GBVRAM ceiling

Weights18 GB

KV cache8.0 GB

Activations0.9 GB

Runtime1.8 GB

Headroom4.7 GB

ESTIMATED DECODE RATE

32 tok/s

Bandwidth-derived estimate · efficiency 0.55. Real-world rates land within ±20% on well-tuned runtimes.

Models that fit your build

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

Comfortable

24 models · ≥15% headroom

Model	Params	Quant	VRAM est.	Context	Evidence	Note
Pollux Judge 32B	32B	Q4_K_M	26.5 GB	4,096	No measured row yet	Fits cleanly at Q4_K_M + 4,096 ctx with 20% headroom.
Qwen 2.5 Coder 32B Instruct	32B	Q4_K_M	22.1 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 33% headroom.
Sarvam 30B	30B	Q4_K_M	24.8 GB	4,096	No measured row yet	Fits cleanly at Q4_K_M + 4,096 ctx with 25% headroom.
Muse Glimmer 30B	30B	Q4_K_M	18.6 GB	8,192	No measured row yet	Comfortable fit with 44% headroom — room to extend context or run alongside other workloads.
Gemma 4 Turkish 26B (4B active)	26B	Q4_K_M	28 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 15% headroom.
Mistral Medium 3 24B (dense)	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Mistral Saba 24B	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Mistral Small 3.2 24B	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Devstral Small 2 24B	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Sarvam M	24B	Q4_K_M	19.9 GB	4,096	No measured row yet	Fits cleanly at Q4_K_M + 4,096 ctx with 40% headroom.
DeepSeek R1 Distill Mistral 24B	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Dolphin 3.0 Mistral 24B	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Mistral Small 3 24B	24B	Q4_K_M	26.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 19% headroom.
Codestral 22B	22B	Q4_K_M	24.7 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 25% headroom.
GPT-OSS 20B	21B	MXFP4	25.2 GB	8,192	No measured row yet	Fits cleanly at MXFP4 + 8,192 ctx with 24% headroom.
GPT-OSS Swallow 20B RL v0.1	20B	Q4_K_M	21.6 GB	8,192	No measured row yet	Fits cleanly at Q4_K_M + 8,192 ctx with 35% headroom.
GPT-NeoX 20B	20B	Q4_K_M	14.1 GB	2,048	No measured row yet	Comfortable fit with 57% headroom — room to extend context or run alongside other workloads.
Granite 3 MoE (3B active)	16B	Q4_K_M	18 GB	8,192	No measured row yet	Comfortable fit with 46% headroom — room to extend context or run alongside other workloads.
DeepSeek MoE 16B Base	16B	Q4_K_M	14 GB	4,096	No measured row yet	Comfortable fit with 58% headroom — room to extend context or run alongside other workloads.
DeepSeek Coder V2 Lite (16B)	16B	Q4_K_M	18 GB	8,192	No measured row yet	Comfortable fit with 46% headroom — room to extend context or run alongside other workloads.
DeepSeek V3 Lite (16B MoE)	16B	Q4_K_M	18 GB	8,192	No measured row yet	Comfortable fit with 46% headroom — room to extend context or run alongside other workloads.
DeepSeek V2 Lite Chat	16B	Q4_K_M	16.9 GB	8,192	No measured row yet	Comfortable fit with 49% headroom — room to extend context or run alongside other workloads.
StarCoder 2 15B	15B	Q4_K_M	17 GB	8,192	No measured row yet	Comfortable fit with 49% headroom — room to extend context or run alongside other workloads.
Qwen 2.5 Coder 14B Instruct	14B	Q4_K_M	15.8 GB	8,192	No measured row yet	Comfortable fit with 52% headroom — room to extend context or run alongside other workloads.

Borderline

11 models · tight, may need quant downgrade

Model	Params	Quant	VRAM est.	Context	Evidence	Note
Falcon 40B Instruct	40B	Q4_K_M	28.1 GB	2,048	No measured row yet	Tight fit at Q4_K_M — only 15% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Granite 4.1 30B	30B	Q4_K_M	32.9 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 0% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Granite 4.1 30B Instruct	29B	Q4_K_M	32.7 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 1% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Qwen 3.6 27B (MTP)	27B	Q5_K_M	33 GB	8,192	No measured row yet	Tight fit at Q5_K_M — only 0% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
MedGemma 27B	27B	Q4_K_M	30.3 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 8% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Qwen3.5 27B	27B	Q4_K_M	31.4 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 5% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Qwen3.6 27B	27B	Q4_K_M	31.4 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 5% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Gemma 3 27B	27B	Q4_K_M	30.3 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 8% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
InternVL 2.5 26B	26B	Q4_K_M	29.8 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 10% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Gemma 4 26B MoE	26B	Q4_K_M	29.8 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 10% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Gemma 4 26B-A4B	26B	Q4_K_M	29.8 GB	8,192	No measured row yet	Tight fit at Q4_K_M — only 10% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.

Not practical

16 models · oversize for this build

Model	Params	Quant	VRAM est.	Context	Evidence	Note
Qwen3 Coder 30B-A3B	30B	Q4_K_M	35 GB	8,192	No measured row yet	~35.0 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 6%. Drop quant or move to a larger build.
North Mini Code 1.0	30B	Q4_K_M	35 GB	8,192	No measured row yet	~35.0 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 6%. Drop quant or move to a larger build.
Nemotron 3 Nano (30B-A3B)	30B	Q4_K_M	33.9 GB	8,192	No measured row yet	~33.9 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 3%. Drop quant or move to a larger build.
Qwen 3 30B-A3B	30B	Q4_K_M	33.9 GB	8,192	No measured row yet	~33.9 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 3%. Drop quant or move to a larger build.
Omni 31B Turkish Reasoning	31B	Q4_K_M	33.5 GB	8,192	No measured row yet	~33.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 1%. Drop quant or move to a larger build.
GLM-4.7-Flash	31B	Q4_K_M	35.5 GB	8,192	No measured row yet	~35.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 7%. Drop quant or move to a larger build.
Gemma 4 31B Dense	31B	Q4_K_M	34.4 GB	8,192	No measured row yet	~34.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 4%. Drop quant or move to a larger build.
EXAONE 3.5 32B Instruct	32B	Q4_K_M	34.5 GB	8,192	No measured row yet	~34.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 5%. Drop quant or move to a larger build.
EXAONE 3.5 32B Instruct AWQ	32B	Q4_K_M	34.5 GB	8,192	No measured row yet	~34.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 5%. Drop quant or move to a larger build.
Magistral 32B	32B	AWQ-INT4	36 GB	8,192	No measured row yet	~36.0 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 9%. Drop quant or move to a larger build.
DeepSeek R1 Distill Qwen 3 32B	32B	AWQ-INT4	36 GB	8,192	No measured row yet	~36.0 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 9%. Drop quant or move to a larger build.
EXAONE 4.0.1 32B	32B	Q4_K_M	34.5 GB	8,192	No measured row yet	~34.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 5%. Drop quant or move to a larger build.
Qwen 3 Coder 32B	32B	AWQ-INT4	36 GB	8,192	No measured row yet	~36.0 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 9%. Drop quant or move to a larger build.
Qwen3 Swallow 32B RL v0.2	32B	Q4_K_M	34.5 GB	8,192	No measured row yet	~34.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 5%. Drop quant or move to a larger build.
OLMo 2 32B	32B	Q4_K_M	36 GB	8,192	No measured row yet	~36.0 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 9%. Drop quant or move to a larger build.
llm-jp 4 32B A3B Thinking	32B	Q4_K_M	34.5 GB	8,192	No measured row yet	~34.5 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 5%. Drop quant or move to a larger build.

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.

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