Can I run an LLM on 8GB RAM?

Yes. Qwen 3.5 4B (released March 2026) at Q5_K_M uses about 3GB and gives you a 256K context window with comfortable headroom. The Qwen 3.5 9B at Q4_K_M (about 6GB) is a tight squeeze but viable if you cap context at 16K. Expect 30-50 tokens per second on Apple Silicon and 50-80 tok/s on a discrete 8GB GPU.

Is 8GB enough for OpenClaw?

For autonomous runs, no. Even Qwen 3.5 9B does not pass OpenClaw tool-calling validation reliably at 8GB because there is no headroom for context expansion. Use 8GB for chat or one-shot tasks and configure a cloud fallback for anything that requires multi-step tool calls.

Which 8GB GPU is best for local LLMs?

RTX 4060 Ti 8GB or RTX 3070 are the value picks. The Apple M1 or M2 base (8GB unified) also runs Qwen 3.5 4B and 9B comfortably because the unified memory architecture avoids the PCIe bottleneck.

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Hardware April 26, 2026

Best Local LLMs for 8GB RAM (April 2026): Qwen 3.5 Small Series

8GB is the practical floor for running a useful local LLM. The new Qwen 3.5 small series (released March 2026) gives you a competent 4B model at Q5 with room to spare, or a 9B at Q4 if you can manage context tightly. OpenClaw is not realistic at this tier — use 8GB local for chat and one-shot tasks, with a cloud fallback for tool calling.

Local LLM not enough for your workflow?

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Bottom Line (April 2026)

Best overall pick: Qwen 3.5 4B at Q5_K_M (released March 2026)
Best squeeze for quality: Qwen 3.5 9B at Q4_K_M (tight on context)
Best for code: Qwen 3.5 9B at Q4_K_M
Best tiny model: Qwen 3.5 2B (when speed > quality)
For OpenClaw: Don’t. Use a hosted Ollama Cloud free tier or a paid API for tool calls.

Top Picks for 8GB RAM

1. Qwen 3.5 4B (Q5_K_M) — best general-purpose

Part of the Qwen 3.5 small series released March 2, 2026. About 3GB on disk, 5GB at runtime with 64K context. Strong on chat, decent code, multimodal (text + light vision). Tool calling is functional but not production-grade for autonomous loops.

ollama pull qwen3.5:4b

# Quick test
ollama run qwen3.5:4b "Explain Docker in two sentences"

Expected speed: 40-60 tokens/sec on Apple M1/M2 base, 80-120 tokens/sec on RTX 3070.

2. Qwen 3.5 9B (Q4_K_M) — best quality squeeze

About 5.7GB on disk, 7-7.5GB at runtime with a tight 16K context. The current best-in-class for general capability at this RAM tier. Use this if you want the smartest model that fits.

ollama pull qwen3.5:9b

# Cap context tightly
openclaw config set agents.defaults.context_limit 16000
openclaw chat "Refactor this 50-line script"

Expected speed: 25-35 tokens/sec on Apple Silicon, 50-70 on a 12GB GPU with offload.

3. Qwen 3.5 2B — when speed matters

When you need an instant-response model for classification, summarization, or one-shot Q&A. Roughly 1.4GB at Q5, runs at 80-150 tok/sec on anything modern.

ollama pull qwen3.5:2b

4. gpt-oss 20B (IQ2_XS) — squeeze for tool calling

If you absolutely need OpenAI-style tool-call output and can tolerate IQ2 quality degradation, gpt-oss 20B at IQ2_XS fits in about 6GB. Tool calls still work because gpt-oss has the cleanest JSON schema discipline of any open model. Quality on prose is degraded.

ollama pull gpt-oss:20b-iq2_xs

This is a last-resort option. Prefer Qwen 3.5 9B at Q4 for general use.

What Fits in 8GB

Model	Quant	RAM Used	Context That Fits
Qwen 3.5 2B	Q5_K_M	~2 GB	128K
Qwen 3.5 4B	Q5_K_M	~3.5 GB	64K
Qwen 3.5 4B	Q8_0	~5 GB	32K
Qwen 3.5 9B	Q4_K_M	~6 GB	16K
gpt-oss 20B	IQ2_XS	~6 GB	16K (degraded)

Common Mistakes at 8GB

Trying to run a 13B model at IQ3. Tool calling collapses, prose degrades. Stick with the Qwen 3.5 small series.
Setting context to 128K on Qwen 3.5 9B. That alone eats 8GB just for the KV cache. Cap at 16K when running locally on tight RAM.
Running parallel inference. Two models loaded means OOM. Quit the one you are not using.
Defaulting to Llama 3.1 8B. It still works, but Qwen 3.5 9B is meaningfully better and ships with a longer context window. Old guides recommended Llama because Qwen 3.5 9B did not exist before March 2026.

OpenClaw on 8GB: The Honest Take

OpenClaw’s tool-calling loop expects clean JSON arguments dozens of times per session. Even Qwen 3.5 9B drifts after a few rounds when context fills up at 16K cap. The recommended setup:

# Local for short tasks
openclaw chat "Rename file to lowercase"  # → ollama/qwen3.5:9b is fine

# Cloud for autonomous runs
openclaw run --agent --model openrouter/qwen/qwen-3.6-27b "Refactor this module"

🛒 Recommended hardware for local AI

The two Macs that handle the workloads on this page.

MINIMUM · 24 GB

Apple MacBook Pro M-series 24GB — minimum for seamless local AI

Apple MacBook Pro M-series The 24 GB unified memory floor for seamless local AI. Runs 27B models at Q4 comfortably; perfect entry rig for OpenClaw + Ollama.

View on Amazon → RECOMMENDED · 48 GB+

MacBook Pro M-series — recommended Mac for serious local AI workloads

Premium Mac for 48 GB+ For 70B-class models, multi-model setups, and 8-hour autonomous OpenClaw loops without compromise.

View on Amazon →

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Hardware That Actually Hits 8GB

Apple Mac mini M4 (16GB) — base model has 16GB unified, gives you headroom even at 9B Q5
M1/M2 MacBook Air (8GB) — runs Qwen 3.5 4B Q5 at 30-40 tok/sec
RTX 3070 / RTX 4060 Ti 8GB — discrete option for Linux/Windows