What is the best local LLM for an RTX 4090?

As of August 2026, Gemma 4 26B-A4B at Q4_K_M is the best all-round pick for a 24 GB RTX 4090. It is a 26B sparse Mixture-of-Experts model with only about 4B parameters active per token, so it uses roughly 17 GB at Q4_K_M and generates at around 85 tokens/sec while supporting 256K context and multimodal input. Qwen 3.6 27B at Q4_K_M remains the best dense model for reasoning quality (about 17 GB, ~50 tokens/sec). For agentic coding, Laguna XS 2.1 at Q4_K_M is the pick. For OpenClaw production loops, gpt-oss 20B is the safer choice due to cleaner JSON tool calls.

Can the RTX 4090 run Gemma 4 26B-A4B?

Yes, comfortably. Gemma 4 26B-A4B at Q4_K_M is about 16.9 GB, leaving roughly 7 GB of the 4090's 24 GB free for KV cache. Because it is a Mixture-of-Experts model activating only about 4B params per token, it generates far faster than a dense 26B would — around 85 tokens/sec on the 4090. You can push context well past 32K on this model where a dense 27B would OOM.

RTX 4090 vs RTX 3090 — is the upgrade worth it for LLMs?

Strictly for LLMs: only if you do a lot of interactive chat where seconds matter. The 4090 is ~40% faster on inference (1008 vs 936 GB/s bandwidth). For OpenClaw autonomous loops where you're not watching tokens stream, the gap matters less. Used 3090s give you about 70% of the 4090's LLM throughput, but the price gap has closed: as of August 2026 a used 3090 is $1,000-1,300 and a used 4090 is roughly $2,270-2,600, so the 3090 is about half the price rather than the third it was when 3090s sold at $600-800. Neither card is depreciating much, because AI demand is holding a floor under both.

Can the RTX 4090 run Llama 3.3 70B?

Same answer as 3090: only at degraded quants. 70B at Q3 needs ~28 GB; at IQ2_XS (~19 GB) it fits but quality collapses. For 70B-class on a single 24 GB GPU, you can't. Buy a 5090 (32 GB) or run two 4090s in tandem, or use Mac Studio M2 Ultra unified memory.

← All guides

Hardware May 18, 2026

Best Local LLM for RTX 4090 (August 2026): 24GB VRAM Picks + OpenClaw Setup

The RTX 4090 is the bandwidth king for 24 GB workloads. As of August 2026 the fastest good pick is Gemma 4 26B-A4B — a sparse MoE that only activates 4B params per token, so it runs at roughly 85 tokens/sec at Q4_K_M while using about 16 GB. Qwen 3.6 27B is still the best dense model for quality. If you bought a 4090 for gaming, OpenClaw + Ollama turn it into a serious local AI rig.

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🎮 THE RTX 4090 — AND WHERE TO GO NEXT

The RTX 4090's 24 GB runs Gemma 4 26B-A4B at ~85 tok/sec and Qwen 27B-class models fast at Q4. Want more headroom? The 5090 steps up to 32 GB. On a budget, a used 3090 gives the same 24 GB for less.

4090GIGABYTE RTX 4090 24 GB ↗ 5090GIGABYTE RTX 5090 32 GB ↗ 3090EVGA RTX 3090 24 GB ↗

Bottom Line

Updated August 8, 2026: Gemma 4 26B-A4B (Google, June 2026) is the new overall pick — MoE, ~16 GB at Q4, ~85 tok/sec, 256K context, multimodal. Laguna XS 2.1 added as the agentic-coding pick.

Best overall pick: Gemma 4 26B-A4B at Q4_K_M (~16 GB, ~85 tok/sec, 256K context)
Best dense model for reasoning: Qwen 3.6 27B at Q4_K_M (~17 GB, ~50 tok/sec)
Best agentic coding: Laguna XS 2.1 at Q4_K_M (~20 GB — 33B MoE, 3B active, 70.9% SWE-Bench Verified)
Best for OpenClaw production: gpt-oss 20B (cleanest tool calls, ~14 GB at MXFP4)
vs RTX 3090: ~40% faster on identical workloads, same 24 GB ceiling

If your query was specifically “best local LLM reddit RTX 4090” or “best model for 4090 reddit”, use the compressed Reddit-intent version too: Best local LLM Reddit users recommend for RTX 4090. It gives the short community-search answer before this deeper hardware guide.

Top Picks for RTX 4090 (24 GB VRAM, 1008 GB/s bandwidth)

1. Gemma 4 26B-A4B (Q4_K_M) — best overall

Google’s June 2026 release, and the pick most 24 GB guides now lead with. It is a 26.1B sparse Mixture-of-Experts model where only about 4B parameters activate per token, so you get 26B-class quality at small-model speed. The Q4_K_M build is roughly 16.9 GB, which leaves about 7 GB free on a 4090 for KV cache — enough to actually use its 256K context instead of capping at 32K like the dense models below. It is also multimodal, so it handles image input that Qwen 3.6 27B does not.

ollama pull gemma4:26b-a4b
openclaw config set agents.defaults.models.chat ollama/gemma4:26b-a4b

Expected speed on RTX 4090: ~85 tokens/sec.

2. Qwen 3.6 27B (Q4_K_M) — best dense model for reasoning

Released April 22, 2026. Outperforms the 397B Qwen 3.5 MoE on agentic coding (77.2 SWE-Bench Verified). About 17 GB VRAM at Q4_K_M with 32K context.

ollama pull qwen3.6:27b
openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b

Expected speed on RTX 4090: 45-55 tokens/sec.

3. Laguna XS 2.1 (Q4_K_M) — best agentic coding

Poolside’s July 2, 2026 release. 33B-total MoE with 3B active params per token, built for agentic coding and long-horizon work. Scores 70.9% on SWE-Bench Verified. The Q4_K_M build is 20 GB — it fits the 4090’s 24 GB, but headroom is tight, so cap context at 8-16K rather than chasing the model’s native 256K.

ollama pull laguna-xs-2.1
openclaw config set agents.defaults.models.chat ollama/laguna-xs-2.1

Only 3B params are active per token, so generation runs at small-model speed despite the 33B total size.

4. gpt-oss 20B (Q5_K_M) — best for OpenClaw production

OpenAI’s 20B at Q5 uses about 15 GB. Cleanest tool-call JSON of any open-weight model.

ollama pull gpt-oss:20b-q5_K_M
openclaw config set agents.defaults.models.chat ollama/gpt-oss:20b-q5_K_M

5. Qwen 3.6 35B-A3B (Q5_K_M) — fastest dense-quality MoE

MoE variant — 3B active params per token. At Q5 uses about 22 GB. Inference is blistering on the 4090: 65-75 tok/sec.

6. Qwen 3.6 27B (Q5_K_M) — premium quality squeeze

Q5_K_M of the same 27B model uses ~19 GB. Slight quality bump over Q4, ~30% slower (35-45 tok/sec). Worth it if your workload is reasoning-heavy.

What Fits in 24 GB VRAM (RTX 4090)

Model	Quant	VRAM	Tok/sec
Gemma 4 26B-A4B (MoE)	Q4_K_M	~16.9 GB	~85
Qwen 3.6 27B	Q4_K_M	~17 GB	45-55
Laguna XS 2.1 (MoE) ← coding	Q4_K_M	~20 GB	50-65
Qwen 3.6 27B	Q5_K_M	~19 GB	35-45
Qwen 3.6 35B-A3B (MoE)	Q5_K_M	~22 GB	65-75
gpt-oss 20B	Q5_K_M	~15 GB	55-70
Qwen 3.5 9B	Q8_0	~10 GB	90-110

OpenClaw Setup on RTX 4090

ollama pull gemma4:26b-a4b
openclaw config set agents.defaults.models.chat ollama/gemma4:26b-a4b
openclaw config set agents.defaults.context_limit 65536
openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q5_K_M
openclaw chat "Refactor the auth module"

Common Mistakes on RTX 4090

Defaulting to Q8 because you can. Q5_K_M is near-FP16 quality. Q8 just halves your tokens/sec for imperceptible gain on 27B models.
Running Llama 3.3 70B at IQ2. Qwen 3.6 27B at Q5 beats it on benchmarks for half the VRAM. The 70B obsession is mostly outdated for 2026.
Setting context to 128K on a dense model. KV cache eats 8-12 GB on top of a dense 27B. You’ll OOM. Cap at 64K. The exception is Gemma 4 26B-A4B — at ~16.9 GB it leaves enough headroom to actually run long context on 24 GB.
Forgetting the 4090 pulls 450W. Use a 1000W+ PSU with at least 100W headroom for sustained inference loads.

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