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, 32B dense models at Q4_K_M already land right at that ceiling once KV cache and overhead are counted, not just the weights: real totals run 23-25 GB even at a modest 8K context. Some fit with a hair of room; most need a step down to Q3_K_M or a bit of CPU offload once you add real 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 is the practical ceiling for single 24 GB cards, and where local models start genuinely competing with cloud APIs -- though 'fits' is tighter than it looks once KV cache and overhead are counted, not just the weights. Qwen 3.6 27B at Q4 (18.7 GB total) is the consensus #1 pick with real headroom to spare, scoring 77.2 on SWE-bench Verified (tying Claude Sonnet 4.5). Gemma 4 31B at Q4 (22.3 GB total) also fits, with only a sliver of room left. DeepSeek R1 Distill Qwen 32B (23.4 GB total) adds chain-of-thought reasoning at 30-42 tok/s on the RTX 4090, and Qwen3 32B (23.1 GB total) is a strong general-purpose alternative, but both real totals land just past a 24 GB card's ~22.8 GB usable capacity -- expect a step down to Q3_K_M or a bit of CPU offload, not a clean native fit. The key takeaway: at 24 GB, you can run genuinely capable models that approach frontier quality for most everyday tasks, but the 32B-class options specifically sit right at the edge, not comfortably inside it.
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. Even at Q2_K, Llama 3.3 70B's real total (weights + KV cache + overhead) is about 31 GB -- past a real 24 GB card's ~22.8 GB usable capacity, so this isn't actually a native fit at any quant. Running 70B at Q2 on a single 24 GB card means real CPU offload, not just an aggressive quant, and offload comes with its own speed penalty on top of Q2_K's quality hit. Q2_K also suffers significant quality degradation on its own terms: outputs become shorter, less coherent, lose factual precision. The 32B at Q4 preserves far more of the original quality and comes far closer to a clean native fit. The quantized 70B 'knows more' (wider knowledge base) but 'thinks worse' (degraded reasoning) and runs slower from the offload. The exception: if you need the 70B's broader knowledge for factual recall and can tolerate both degraded reasoning and offload-slowed generation, Q2 can still work.
MoE Models: 24 GB's Hidden Advantage
MoE models shine on 24 GB for speed, though VRAM headroom is just as tight here as the dense 32B class once KV cache and overhead are counted. Qwen3.5 35B-A3B's real Q4_K_M total is about 24 GB, just past a real 24 GB card's ~22.8 GB usable capacity, but it still achieves 112 tok/s on RTX 3090 and 207 tok/s on RTX 4090 with a small step down or a touch of offload -- near-32B-dense quality at small-model speeds. Nemotron 3 Nano, NVIDIA's 32B-total/3B-active MoE (mmluPro 78.3), is similar: its recommended Q5_K_M quant's real total is about 25.8 GB, also past the real 24 GB ceiling, but it was built specifically for efficient local deployment and is worth the small offload. With only 3-4B active parameters per token, MoE models like these generate tokens at speeds comparable to a 3-4B dense model while delivering quality near a 32B dense model, even when a sliver of them spills to system RAM. For agentic coding workflows, MoE on 24 GB is one of the optimal mid-2026 configurations.
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) has almost identical memory bandwidth (1008 vs 936 GB/s), so raw token-generation speed is only about 8% higher, not the 30-40% gap you'd expect from gaming benchmarks. Where the 4090 actually pulls ahead is prompt processing: its much higher compute throughput chews through long or repeatedly-growing context noticeably faster, which is what matters for agentic workflows re-processing 10K+ tokens of context each turn. RX 7900 XTX ($750-850 used, 960 GB/s) offers 4090-competitive bandwidth at lower price (~96 tok/s on 8B, 75% of 4090's practical throughput once ROCm's software overhead is factored in). 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 ~49 GB total; multiple GPUs required. Mixtral 8x7B Q4 needs ~32.5 GB total, more than the weights alone (28.4 GB) suggest once KV cache and overhead are added. Llama 4 Scout (109B MoE) Q4 needs ~76 GB total. For 70B Q4, upgrade to three 3090s (72 GB, ~$2100-2700) 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 (18.7 GB total) 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, though both real totals land closer to the 24 GB ceiling and may need a small step down or offload.
- Can I run Llama 3.3 70B on a single RTX 4090?
- At Q4_K_M (~49 GB total), no, not even close on a 24 GB card. At Q2_K (~31 GB total) it still doesn't fit a single 4090, though it's closer; quality also degrades badly at Q2_K. 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.