Best LLMs for 24 GB VRAM (2026)
24 GB: Where You Stop Compromising
24 GB of VRAM is the community sweet spot for local LLMs in 2026. With ~23 GB usable, you can run 32B dense models at Q4_K_M (~20 GB) comfortably with room for 8K-32K context. The RTX 3090 (used, $700-900, 936 GB/s) is the consensus value king. The RTX 4090 ($1500-2000 used, 1008 GB/s) is ~40% faster but offers no VRAM advantage. A Reddit thread asking best model for 24GB in 2026 garnered 98 upvotes and 61 detailed answers — this is the most active tier in the community.
32B at Q4: The Sweet Spot Ceiling
The 32B class at Q4_K_M (~20 GB) is the practical ceiling for single 24 GB cards — and where local models start genuinely competing with cloud APIs. Qwen 3.6 27B at Q4 (~17 GB) is the consensus #1 pick, scoring 77.2 on SWE-bench Verified (tying Claude Sonnet 4.5). DeepSeek R1 Distill Qwen 32B at Q4 (~20 GB) adds chain-of-thought reasoning at 30-42 tok/s on the RTX 4090. Qwen3 32B at Q4 (~20 GB) and Gemma 4 31B at Q4 (~19 GB) are strong general-purpose alternatives. The key takeaway: at 24 GB, you can run genuinely capable models that approach frontier quality for most everyday tasks.
The 70B Q2 vs 32B Q4 Debate
This is the most contentious question on 24 GB. The community consensus: run 32B at Q4, not 70B at Q2. At Q2_K, Llama 3.3 70B fits in ~20 GB but suffers significant quality degradation — outputs become shorter, less coherent, lose factual precision. The 32B at Q4 preserves far more of the original quality. The quantized 70B 'knows more' (wider knowledge base) but 'thinks worse' (degraded reasoning). The exception: if you need the 70B's broader knowledge for factual recall and accept degraded reasoning, Q2 can work.
MoE Models: 24 GB's Hidden Advantage
MoE models shine on 24 GB. Qwen3.5 35B-A3B at Q4_K_M (~20 GB) achieves 112 tok/s on RTX 3090 and 207 tok/s on RTX 4090 — near-32B-dense quality at small-model speeds. Qwen3.6 35B-A3B MoE at Q4 runs at 165-234 tok/s on RTX 5090. With only 3B active parameters per token, MoE models generate tokens at speeds comparable to a 3B dense model while delivering quality near a 32B dense model. For agentic coding workflows, MoE on 24 GB is the optimal mid-2026 configuration.
RTX 3090 vs 4090 vs 7900 XTX
The RTX 3090 (used, $700-900) is the single most-recommended GPU in the community. RTX 4090 ($1500-2000 used) is ~40% faster — meaningful for agentic workflows generating 10K+ tokens. RX 7900 XTX ($750-850 used, 960 GB/s) offers 4090-competitive bandwidth at lower price (~96 tok/s on 8B, 75% of 4090). Trade-off: ROCm setup friction, missing CUDA-exclusive tools. For pure llama.cpp/Ollama, the 7900 XTX is excellent value. For broader ecosystem, the 3090 is safer.
What You Cannot Do with 24 GB
Llama 3.3 70B at Q4 needs ~39 GB — dual GPUs required. Mixtral 8x7B Q4 needs ~28 GB. Llama 4 Scout (109B MoE) Q4 needs ~55 GB. For 70B Q4, upgrade to dual 3090s (48 GB, ~$1500-1800) or Apple M4 Max 128GB. For most users, 32B class at Q4 on 24 GB covers 85-90% of local LLM use cases.
Frequently asked questions
- What is the single best model for 24 GB?
- Qwen 3.6 27B at Q4_K_M (~17 GB) is the consensus #1 pick mid-2026. SWE-bench 77.2 (ties Sonnet 4.5), fits with room for 32K+ context. Gemma 4 31B and Qwen3 32B are strong alternatives.
- Can I run Llama 3.3 70B on a single RTX 4090?
- At Q4_K_M (~39 GB), no. At Q2_K (~20 GB) it fits but quality degrades. Community consensus: run 32B at Q4 instead of 70B at Q2.
- Used RTX 3090 vs new RTX 4070 Ti Super?
- 3090 wins: 24 GB vs 16 GB. That 8 GB determines whether you can run 32B at Q4. VRAM capacity > generation speed for LLM inference.
- Is RTX 4090 worth the premium over 3090?
- For 7-14B: no, 3090 is fast enough. For 32B with agentic workflows: 4090's 40% speed advantage matters. Both have identical 24 GB ceiling.