ComputeAtlas

48GB local-LLM model-fit guide

RTX 6000 Ada for Local LLMs: What Fits in 48GB VRAM?

The RTX 6000 Ada provides 48GB of ECC GDDR6 on one professional workstation GPU. That larger single-card memory envelope can keep some quantized 70B-class and sparse-MoE planning targets on one board, but capacity alone does not guarantee a specific runtime, context length, or tokens-per-second result.

Verified RTX 6000 Ada hardware baseline

GPU memory

48GB GDDR6 ECC

Maximum power

300W

PCIe interface

PCI Express Gen 4 x16

Form factor

Dual-slot

Thermal

Active

Manufacturer specification verified 2026-10-03. The ComputeAtlas catalog is build-gated against the 48GB / 300W professional-workstation baseline.

What local LLM planning targets fit inside 48GB?

This table is generated from the same MODEL_PROFILES registry used by the ComputeAtlas AI Hardware Estimator. A green result means the standard inference planning target is at or below 48GB; it is not a runtime guarantee, benchmark claim, or promise that every context length and software stack will fit.

ModelPrecisionComputeAtlas planning target48GB resultCapacity margin
Llama 3 8BFP1618GBWithin 48GB planning reference30GB remaining versus the planning target
Llama 3 8B8-bit10GBWithin 48GB planning reference38GB remaining versus the planning target
Llama 3 8B4-bit5GBWithin 48GB planning reference43GB remaining versus the planning target
Llama 3 70BFP16154GBExceeds 48GB single-GPU reference106GB above one-card capacity
Llama 3 70B8-bit81GBExceeds 48GB single-GPU reference33GB above one-card capacity
Llama 3 70B4-bit42GBWithin 48GB planning reference6GB remaining versus the planning target
Mixtral 8x7BFP16104GBExceeds 48GB single-GPU reference56GB above one-card capacity
Mixtral 8x7B8-bit55GBExceeds 48GB single-GPU reference7GB above one-card capacity
Mixtral 8x7B4-bit29GBWithin 48GB planning reference19GB remaining versus the planning target
DeepSeek LLM 67BFP16148GBExceeds 48GB single-GPU reference100GB above one-card capacity
DeepSeek LLM 67B8-bit78GBExceeds 48GB single-GPU reference30GB above one-card capacity
DeepSeek LLM 67B4-bit41GBWithin 48GB planning reference7GB remaining versus the planning target
Qwen3 8BFP1618GBWithin 48GB planning reference30GB remaining versus the planning target
Qwen3 8B8-bit10GBWithin 48GB planning reference38GB remaining versus the planning target
Qwen3 8B4-bit5GBWithin 48GB planning reference43GB remaining versus the planning target
Qwen3 32BFP1673GBExceeds 48GB single-GPU reference25GB above one-card capacity
Qwen3 32B8-bit38GBWithin 48GB planning reference10GB remaining versus the planning target
Qwen3 32B4-bit20GBWithin 48GB planning reference28GB remaining versus the planning target
Mistral Small 3.1 24BFP1655GBExceeds 48GB single-GPU reference7GB above one-card capacity
Mistral Small 3.1 24B8-bit28GBWithin 48GB planning reference20GB remaining versus the planning target
Mistral Small 3.1 24B4-bit15GBWithin 48GB planning reference33GB remaining versus the planning target
Gemma 3 27BFP1660GBExceeds 48GB single-GPU reference12GB above one-card capacity
Gemma 3 27B8-bit32GBWithin 48GB planning reference16GB remaining versus the planning target
Gemma 3 27B4-bit17GBWithin 48GB planning reference31GB remaining versus the planning target
DeepSeek-R1-Distill-Qwen-32BFP1671GBExceeds 48GB single-GPU reference23GB above one-card capacity
DeepSeek-R1-Distill-Qwen-32B8-bit37GBWithin 48GB planning reference11GB remaining versus the planning target
DeepSeek-R1-Distill-Qwen-32B4-bit20GBWithin 48GB planning reference28GB remaining versus the planning target
DeepSeek-R1 671BFP161477GBExceeds 48GB single-GPU reference1429GB above one-card capacity
DeepSeek-R1 671B8-bit772GBExceeds 48GB single-GPU reference724GB above one-card capacity
DeepSeek-R1 671B4-bit403GBExceeds 48GB single-GPU reference355GB above one-card capacity
gpt-oss-20bFP1647GBWithin 48GB planning reference1GB remaining versus the planning target
gpt-oss-20b8-bit24GBWithin 48GB planning reference24GB remaining versus the planning target
gpt-oss-20b4-bit16GBWithin 48GB planning reference32GB remaining versus the planning target
gpt-oss-120bFP16258GBExceeds 48GB single-GPU reference210GB above one-card capacity
gpt-oss-120b8-bit135GBExceeds 48GB single-GPU reference87GB above one-card capacity
gpt-oss-120b4-bit80GBExceeds 48GB single-GPU reference32GB above one-card capacity

The 48GB decision boundary

When 48GB is enough

If the complete standard inference planning target is below 48GB, the RTX 6000 Ada has a credible single-card capacity case. Quantization can materially change that result, especially for 67B–70B-class models.

When 48GB is tight

A planning target in the low-40GB range leaves limited margin for larger contexts, alternate runtimes, adapters, or concurrency. Validate the exact software stack before procurement.

When 48GB is not enough

If the governed target exceeds 48GB, move to offload, a software-aware multi-GPU topology, or a higher-memory single GPU instead of pretending the workload cleanly fits.

Why power and form factor still matter

The RTX 6000 Ada pairs 48GB ECC memory with a 300W dual-slot professional form factor, which changes workstation density and power planning compared with large open-air consumer cards.

What two RTX 6000 Ada cards do — and do not — give you

Two boards provide 96GB aggregate VRAM, but that is not one transparent 96GB memory pool. Separate workers can use one GPU each; a single model spanning both cards needs software-aware model parallelism or sharding that your chosen runtime actually supports.

  • Use dual GPUs for parallel model workers, evaluation jobs, RAG services, or explicitly supported sharding.
  • Do not add the two 48GB boards together and describe every workload as if it sees one automatic 96GB device.
  • Validate PCIe topology, slot spacing, power, cooling, and software placement before treating a second GPU as a simple capacity upgrade.

Move from model fit into a workstation plan

Open 1× RTX 6000 Ada LLM planOpen 2× RTX 6000 Ada RAG planOpen 2× RTX 6000 Ada development planHow much GPU do I need?

Evidence and related decision tools

LLM VRAM requirementsGPU Comparison StudioRTX 5090 vs RTX 4090 local LLM guide