Best Local LLMs for a 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek
Six local LLMs that fit a single 24GB GPU in 2026: memory breakdowns, quantization tips, and which model fits which workload.
A single 24GB GPU, whether an RTX 3090 or RTX 4090, is enough to run genuinely capable language models for coding, chat, and agent workflows in 2026. The old advice of squeezing the largest 70B quantized model onto the card is now outdated. Modern 20B to 35B class models fit cleanly, respond quickly, and leave room for context. This guide covers the six best options, how VRAM actually gets allocated, and which runtime to use for each job.
What happened
| Model | Key fact |
|---|---|
| Qwen3.6-27B | 27B dense, ~16GB at Q4_K_M, Apache 2.0, released April 2026 |
| Qwen3.6-35B-A3B | 35B total MoE, 3B active, ~20GB at Q4_K_M |
| Gemma 4 26B | 26B MoE, 3.8B active, vision + 140+ languages, Apache 2.0, April 2, 2026 |
| Mistral Small 3.2 24B | 24B dense, ~14GB at Q4_K_M, Apache 2.0 |
| gpt-oss-20b | 21B MoE, 3.6B active, native MXFP4, ~14GB, Apache 2.0 |
| DeepSeek-R1-Distill-Qwen-32B | 32B dense, ~18-20GB at Q4_K_M, MIT license |
How 24GB of VRAM actually gets spent
Three things consume memory during inference. The first is model weights. At Q4_K_M, the most common home-inference quantization format, each parameter costs roughly 0.58 bytes. A 32B model therefore needs around 18 to 20GB in weights alone.
The second is the KV cache, which stores key-value attention states and grows with context length. Longer sessions eat more VRAM fast. The third is runtime overhead from whatever serving stack you use. A safe working estimate adds about 1 to 2GB for the cache and runtime at short context.
MoE (Mixture-of-Experts) models are a common trap. In an MoE architecture, only a fraction of the model’s “expert” sub-networks fire for each token, which makes decoding faster. But all experts still live in VRAM. So you size MoE memory by total parameters, never by the smaller active count. Q4_K_M is the standard balance of quality and footprint. Q5_K_M and Q6_K raise quality at a memory cost. Q8_0 and BF16 are typically too large for 30B-class models on one 24GB card.
The six models worth running
Qwen3.6-27B: best all-around default
Alibaba’s Qwen3.6-27B is described as the strongest single default for a 24GB card. It is a dense 27B model released in April 2026 under Apache 2.0, focused on agentic coding, repository-level reasoning, and frontend workflows. At Q4_K_M it needs roughly 16GB, leaving comfortable headroom for longer context windows.
Qwen3.6-35B-A3B: fastest for general chat and tool use
This is a Mixture-of-Experts model with 35B total parameters but only about 3B active per token. Decoding is much faster than a comparable dense model because only a fraction of weights fire at each step. Memory still tracks total parameters, so plan for around 20GB at Q4_K_M. It is a tight but valid fit on a 24GB card.
Gemma 4 26B: multimodal and multilingual
Google DeepMind released Gemma 4 on April 2, 2026, under Apache 2.0. According to the DeepMind Gemma page, this was the first Gemma generation to ship with a fully open license. The 26B variant is an MoE with 3.8B active parameters. It supports vision input and coverage of more than 140 languages, making it the natural pick when your workload includes images or non-English text.
Mistral Small 3.2 24B: lowest footprint, most headroom
Mistral’s Small line targets low-latency assistant workloads. Small 3.1 added multimodal input and a longer context window; Small 3.2 refined instruction following. At roughly 14GB at Q4_K_M, it is the lightest model in this group. That headroom lets you push context further than the heavier 32B options allow. Mistral also shipped larger successors in 2026, but those exceed the single-card tier.
gpt-oss-20b: easy reasoning fallback
OpenAI’s gpt-oss-20b is an open-weight reasoning model under Apache 2.0. It is an MoE design with 21B total parameters and 3.6B active per token. It ships in a native MXFP4 4-bit format and loads in roughly 14GB with generous headroom. It is strong at structured reasoning and tool use, and weaker on broad world knowledge.
DeepSeek-R1-Distill-Qwen-32B: deepest reasoning, tightest fit
DeepSeek distilled its R1 reasoning traces into a set of smaller dense models. The 32B variant is built on a Qwen2.5 base and released under an MIT license. At Q4_K_M it uses about 18 to 20GB, the tightest fit in this group. It exposes its chain-of-thought process through visible reasoning tokens, which is useful for slow, deliberate problem solving. Pre-built GGUF files are available from bartowski on Hugging Face.
What does not fit on a single 24GB card?
The frontier open models of 2026 are large sparse MoE systems that require multi-GPU rigs or high-memory unified machines. None of them run on one consumer card:
- GLM-5.2 from Z.ai: roughly 753B total parameters
- Moonshot’s Kimi K2.7: around 1 trillion total parameters
- DeepSeek V4-Pro checkpoint: near 1.6 trillion parameters
- Alibaba’s Qwen3.5-397B and Mistral Large 3: both in the server-class range
These are worth knowing as API options. They do not change what runs locally on a single card. For context on how the frontier MoE race is shaping up, see our coverage of Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2 benchmarks and costs.
What to do about it
- Identify your primary workload: coding and agents (Qwen3.6-27B), fast general chat (Qwen3.6-35B-A3B), multimodal or multilingual (Gemma 4 26B), long-context assistant (Mistral Small 3.2), structured reasoning (DeepSeek-R1-Distill-Qwen-32B).
- Start with Ollama if you want the quickest setup. It handles quantization selection automatically and exposes an OpenAI-compatible API.
- Switch to llama.cpp if you need fine control over GGUF quantization levels or GPU layer offloading.
- Use vLLM if you are serving multiple concurrent users or building a heavier agent pipeline.
- Check total parameters, not active parameters, before loading any MoE model. A “3B active” label does not mean a 3B memory footprint.
Our take
The gap between a well-chosen local model and an API call is narrower than most people expect in 2026. Qwen3.6-27B at Q4_K_M fits in 16GB, responds fast enough for interactive use, and carries a permissive Apache 2.0 license. For businesses handling sensitive data or wanting predictable inference costs, that is a serious option, not a hobbyist experiment.
The MoE memory trap is the most common mistake we see. Someone loads a “35B” model expecting 35B-worth of capability at a low memory cost, then wonders why it barely fits. Total parameters are what fill VRAM. Always check that number first.
If you are exploring how local models fit into a broader product or workflow, our AI integration services cover deployment, testing, and connecting models to existing business systems. For teams already running agent pipelines, workflow automation is often the faster path to practical output than raw inference tuning.
Frequently asked questions
What is the best LLM to run on a 24GB GPU in 2026?
Qwen3.6-27B is recommended as the strongest all-around default. It is a dense 27B model that uses roughly 16GB at Q4_K_M quantization, leaving headroom for context. It is released under Apache 2.0 and focuses on agentic coding and reasoning tasks.
How much VRAM does a 32B model use at Q4_K_M?
At Q4_K_M quantization, each parameter costs roughly 0.58 bytes, so a 32B model needs about 18 to 20GB just for weights. Add 1 to 2GB for the KV cache and runtime overhead, making it a tight fit on a 24GB card.
Do MoE models use less VRAM because fewer experts are active?
No. In Mixture-of-Experts models, all experts remain resident in VRAM even though only a fraction fire per token. You must size memory by the total parameter count, not the smaller active count. The speed benefit is real; the memory saving is not.
Which runtimes can run these local LLMs?
Three runtimes cover most setups: Ollama (simplest, automatic quantization selection, OpenAI-compatible API), llama.cpp (fine control over GGUF quantization and GPU offload), and vLLM (throughput-focused for concurrent workloads).
