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
96 GB
Effective VRAM
~76 GB
range 72-78 GB
Topology
single node multi gpu
pcie
Setup difficulty
advanced
speed penalty ~18%
Why effective VRAM is lower than total

4× NVIDIA GeForce RTX 3090 = 96 GB total VRAM, but without NVLink, cross-card bandwidth is PCIe-bound (~32 GB/s vs NVLink ~112 GB/s). With tensor-parallelism, each card holds ~1/4 of the model weights and replicates activations + KV cache. After 15% TP overhead, effective model capacity is ~76 GB. Largest single tensor on one card is ~22 GB.

Recommended runtime

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

vLLM
primary
involved

Tensor-parallel across NVLink/PCIe — works on every recent consumer + datacenter pair. AWQ-INT4 + 70B fits dual 3090 / dual 4090 cleanly.

ExLlamaV2
alternative
involved

Single-stream king. EXL2 4.0bpw + 70B fits dual 3090 with NVLink and beats vLLM on solo-user throughput.

llama.cpp
alternative
moderate

Layer-split via --tensor-split is the experimentation-friendly path. Worse throughput than vLLM but easier to debug.

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
Llama 3.3 70B Instruct70BQ5_K_M63.2 GB8,192Fits cleanly at Q5_K_M + 8,192 ctx with 17% headroom.
Aya 23 35B35BQ4_K_M49.7 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 35% headroom.
Command R 35B35BQ4_K_M49.7 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 35% headroom.
Phind CodeLlama 34B v234BQ4_K_M48.5 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 36% headroom.
Yi 1.5 34B34BQ4_K_M48.5 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 36% headroom.
DeepSeek Coder V333BAWQ-INT461.1 GB8,192Fits cleanly at AWQ-INT4 + 8,192 ctx with 20% headroom.
Qwen 2.5 32B Instruct32BQ8_061.7 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 19% headroom.
DeepSeek R1 Distill Qwen 3 32B32BAWQ-INT459.6 GB8,192Fits cleanly at AWQ-INT4 + 8,192 ctx with 22% headroom.
QwQ 32B Preview32BQ4_K_M46.3 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 39% headroom.
EXAONE 3.5 32B32BAWQ-INT459.6 GB8,192Fits cleanly at AWQ-INT4 + 8,192 ctx with 22% headroom.
OLMo 2 32B32BQ4_K_M46.3 GB8,192Fits cleanly at Q4_K_M + 8,192 ctx with 39% headroom.
Aya Expanse 32B32BAWQ-INT459.6 GB8,192Fits cleanly at AWQ-INT4 + 8,192 ctx with 22% headroom.
Qwen 2.5 Coder 32B Instruct32BQ8_047.8 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 37% headroom.
Qwen 3 32B32BQ8_061.7 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 19% headroom.
Magistral 32B32BAWQ-INT459.6 GB8,192Fits cleanly at AWQ-INT4 + 8,192 ctx with 22% headroom.
DeepSeek R1 Distill Qwen 32B32BQ8_061.7 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 19% headroom.
Qwen 3 Coder 32B32BAWQ-INT459.6 GB8,192Fits cleanly at AWQ-INT4 + 8,192 ctx with 22% headroom.
Gemma 4 31B Dense31BQ8_060.1 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 21% headroom.
Qwen 3 30B-A3B30BQ8_058.5 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 23% headroom.
Nemotron 3 Nano (30B-A3B)30BQ8_058.5 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 23% headroom.
Gemma 3 27B27BQ8_053.6 GB8,192Fits cleanly at Q8_0 + 8,192 ctx with 29% headroom.
MedGemma 27B27BQ4_K_M40.6 GB8,192Comfortable fit with 47% headroom — room to extend context or run alongside other workloads.
Gemma 4 26B MoE26BQ4_K_M39.5 GB8,192Comfortable fit with 48% headroom — room to extend context or run alongside other workloads.
InternVL 2.5 26B26BQ4_K_M39.5 GB8,192Comfortable fit with 48% headroom — room to extend context or run alongside other workloads.
Borderline
4 models · tight, may need quant downgrade
ModelParamsQuantVRAM est.ContextNote
Qwen 2.5 Math 72B72BQ4_K_M69.5 GB4,096Tight fit at Q4_K_M — only 9% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Molmo 72B72BQ4_K_M69.5 GB4,096Tight fit at Q4_K_M — only 9% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Jamba 1.5 Mini52BQ4_K_M69 GB8,192Tight fit at Q4_K_M — only 9% headroom. KV cache for longer context will OOM. Cap context tighter or drop one quant level.
Mixtral 8x7B Instruct47BQ5_K_M67.4 GB8,192Tight fit at Q5_K_M — only 11% 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
Nemotron 3 Super 49B49BAWQ-INT485.9 GB8,192~85.9 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 13%. Drop quant or move to a larger build.
Hermes 3 Llama 3.1 70B70BQ4_K_M89.4 GB8,192~89.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Hermes 4 Llama 3.3 70B70BAWQ-INT4118.5 GB8,192~118.5 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 56%. Drop quant or move to a larger build.
DeepSeek R1 Distill Llama 70B70BQ4_K_M89.4 GB8,192~89.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Dolphin 3 Llama 3.3 70B70BAWQ-INT4118.5 GB8,192~118.5 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 56%. Drop quant or move to a larger build.
EVA Llama 3.3 70B70BAWQ-INT4118.5 GB8,192~118.5 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 56%. Drop quant or move to a larger build.
Llama 4 70B70BAWQ-INT4118.5 GB8,192~118.5 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 56%. Drop quant or move to a larger build.
Llama 3.1 Nemotron 70B Instruct70BQ4_K_M89.4 GB8,192~89.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Llama 3.1 70B Instruct70BQ4_K_M89.4 GB8,192~89.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
OpenBioLLM Llama 3 70B70BQ4_K_M89.4 GB8,192~89.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Tulu 3 70B70BQ4_K_M89.4 GB8,192~89.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 18%. Drop quant or move to a larger build.
Qwen 3 72B72BAWQ-INT4121.6 GB8,192~121.6 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 60%. Drop quant or move to a larger build.
Qwen 2.5 72B Instruct72BQ4_K_M91.6 GB8,192~91.6 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 21%. Drop quant or move to a larger build.
Qwen 2.5-VL 72B72BAWQ-INT4121.6 GB8,192~121.6 GB needed at AWQ-INT4 + 8,192 ctx — overshoots effective VRAM by 60%. Drop quant or move to a larger build.
InternVL 2.5 78B78BQ4_K_M98.4 GB8,192~98.4 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 30%. Drop quant or move to a larger build.
Llama 3.2 90B Vision Instruct90BQ4_K_M112 GB8,192~112.0 GB needed at Q4_K_M + 8,192 ctx — overshoots effective VRAM by 47%. 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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