What is the best local LLM for an RTX A6000?

GLM-5.1 32B at Q5_K_M is the best pick for autonomous OpenClaw runs (purpose-tuned for multi-hour agent loops). For general-purpose, Llama 3.3 70B at Q4_K_M uses about 42 GB and gives premium 70B-class quality at ~22 tok/sec. For OpenClaw production reliability, gpt-oss 20B at Q8_0 + Qwen 3.6 27B at Q5 dual-loaded is the strongest setup at this VRAM tier.

RTX A6000 vs 2x RTX 4090 for LLMs?

A6000 wins on simplicity. 2x 4090s give 48 GB total VRAM but split across PCIe with tensor-parallel overhead; setup is complex and many models need code changes. A6000 gives 48 GB unified — any model that fits, runs. The 4090 dual is ~30% faster on bandwidth but requires Linux + careful driver setup. For a managed production OpenClaw host, A6000 is the right call.

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Hardware May 18, 2026

Best Local LLM for RTX A6000 (2026): 48GB Workstation Picks

The NVIDIA RTX A6000 48GB is the single-GPU workstation pick for serious local LLMs. Two consumer 4090s give you 48 GB split across PCIe but with constant headache. The A6000 gives you 48 GB unified at 768 GB/s — enough for Llama 3.3 70B at Q4, GLM-5.1 32B at full Q5, or dual-model OpenClaw routing without compromise.

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RTX A6000 workstation OpenClaw setup?

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Bottom Line

Best for OpenClaw autonomy: GLM-5.1 32B at Q5_K_M
Best general-purpose: Llama 3.3 70B at Q4_K_M (~22 tok/sec)
Best fastest: Qwen 3.6 27B at Q8_0 (~28 tok/sec)
Best production dual: gpt-oss 20B Q8 + Qwen 3.6 27B Q5 (loaded together)

Top Picks for RTX A6000 (48 GB VRAM, 768 GB/s bandwidth)

1. GLM-5.1 32B (Q5_K_M) — best for OpenClaw autonomous runs

Zhipu AI’s GLM-5.1 32B dense at Q5 uses about 26 GB. Purpose-tuned for multi-hour autonomous agent loops with stable JSON tool calls.

ollama pull glm5.1:32b
openclaw config set agents.defaults.models.chat ollama/glm5.1:32b
openclaw run --agent --max-hours 8 "Implement the spec end-to-end"

Expected speed: 22-30 tokens/sec.

2. Llama 3.3 70B (Q4_K_M) — best general-purpose

About 42 GB at Q4_K_M with 16K context. Excellent general knowledge, strong reasoning, clean tool calling. Speed: 18-25 tok/sec on A6000.

ollama pull llama3.3:70b

3. Qwen 3.6 27B (Q8_0) — premium quality at smaller size

Full Q8 of the April 22, 2026 release uses about 30 GB. Near-FP16 quality. Faster than 70B (~28 tok/sec).

4. gpt-oss 20B (Q8_0) + Qwen 3.6 27B (Q5_K_M) — dual-model production setup

Keep both models loaded for instant routing:

# gpt-oss 20B Q8 for agent runs (cleanest tool calls) — 22 GB
# Qwen 3.6 27B Q5 for chat — 20 GB

openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q5_K_M
openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q8_0
openclaw config set agents.defaults.keep_alive 1h
openclaw models status

Total: ~42 GB with both models hot — leaves 6 GB for context.

5. Mistral Small 4 (119B-A6B MoE, IQ3_XS) — squeeze for premium reasoning

Mistral’s March 16, 2026 release. 119B total, 6B active per token. At IQ3_XS uses about 38 GB. MoE design gives faster inference than equivalent dense models.

What Fits in 48 GB VRAM (RTX A6000)

Model	Quant	VRAM	Tok/sec
GLM-5.1 32B	Q5_K_M	~26 GB	22-30
Llama 3.3 70B	Q4_K_M	~42 GB	18-25
Qwen 3.6 27B	Q8_0	~30 GB	25-32
Dual: gpt-oss 20B Q8 + Qwen 3.6 27B Q5	mixed	~42 GB	varies
Mistral Small 4 (119B-A6B)	IQ3_XS	~38 GB	25-35 (MoE)
Qwen 3.6 35B-A3B (MoE)	Q8_0	~38 GB	55-70

OpenClaw Setup on RTX A6000 (dual-model)

ollama pull gpt-oss:20b-q8_0
ollama pull qwen3.6:27b-q5_K_M

openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q5_K_M
openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q8_0
openclaw config set agents.defaults.keep_alive 2h
openclaw config set agents.defaults.context_limit 65536

openclaw models status

Common Mistakes on RTX A6000

Running Llama 3.3 70B at Q6 because you can. Q6 needs ~58 GB — overflows the 48 GB A6000. Cap at Q5 (~50 GB tight) or Q4 (~42 GB comfortable).
Loading 3 models without keep_alive. Ollama unloads idle models in 5 min by default. Set keep_alive 2h so model swaps don’t pause your workflow.
Picking Qwen 3.5 122B-A10B for OpenClaw without fallback. Qwen 3.5 has the Ollama tool-calling bug (issue #14493). Pair with gpt-oss for the agent path.