Best Local LLMs for 24GB GPUs in 2026

In short: A 24GB RTX 3090 or RTX 4090 is still a strong local-LLM tier because NVIDIA lists both cards with 24GB of GDDR6X memory (RTX 3090 specs, RTX 4090 specs). Start with Qwen2.5 32B for general chat (Qwen model card, Ollama tag), Qwen2.5-Coder 32B for code (model card, Ollama tag), DeepSeek-R1-Distill-Qwen-14B for reasoning (model card, Ollama tag), and Mistral Small 3.1 24B when 128K context matters (model card). Treat 70B models as offload experiments on 24GB; Ollama lists Llama 3.3 as a 70B model and the packaged model is larger than a 24GB VRAM budget (Ollama Llama 3.3, Meta Llama 3.3 docs).

The main correction in this refresh: Phi-4 is useful on 24GB, but it is not a 128K-context pick. Microsoft's Phi-4 model card lists 14B parameters, a 16K-token context length, and an MIT license (Microsoft Phi-4 model card). Use Mistral Small 3.1 or a properly configured Qwen long-context setup when long documents are the priority.

This guide keeps the original 24GB-GPU structure: quick start, VRAM budget, quantization, model picks, 3090-vs-4090 buying logic, hardware links, and FAQ. For a broader runtime comparison, see Ollama vs LM Studio vs llama.cpp.

Quick Start

curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5:32b
ollama run qwen2.5:32b

Those commands use Ollama's official install flow and the published Qwen2.5 library tag (Ollama download, qwen2.5 library). After the first run, check actual VRAM use with your runtime logs or nvidia-smi; memory use changes with quantization, context length, batch size, and GPU driver/runtime overhead. llama.cpp documents the quantization tooling behind common GGUF formats such as Q4_K_M and Q5_K_M (llama.cpp quantize docs).

Understanding VRAM Budgets on 24GB

24GB is a model-loading constraint, not a promise that every 24GB-tagged use case will feel fast. NVIDIA's specs confirm the RTX 3090 and RTX 4090 have the same VRAM capacity, while their architectures and memory bandwidth differ (RTX 3090 specs, RTX 4090 specs).

Rule of thumb: 32B models at 4-bit quantization are the practical 24GB sweet spot; 14B models can run at higher quality; 70B models require compromise, offload, or rented/bigger hardware. Treat the rule as a starting point and verify with the exact model file you download.

Quantization: What Fits and What Doesn't

Quantization compresses model weights. The specific file, context length, runtime, and GPU driver decide the final fit, so use this table as a selection guide rather than a universal benchmark.

Top Models for 24GB GPUs (2026)

🏆 1. Qwen2.5 32B — The All-Rounder

Qwen2.5 32B remains the default starting point for 24GB GPUs because it is large enough to feel materially stronger than small 7B/8B models while still fitting the 24GB class with quantization. If you are comparing it against newer Qwen releases, use our Qwen 3.5 vs Qwen 2.5 local LLM comparison before replacing a stable workflow.

ollama pull qwen2.5:32b

💻 2. Qwen2.5-Coder 32B — Best for Coding

Use the Coder variant when the workload is mostly Python, TypeScript, Rust, shell, config, tests, or code review. If you want agent workflow context, pair this local model with the practical tooling comparisons in Claude Code vs Cursor vs Copilot.

ollama pull qwen2.5-coder:32b

🧮 3. DeepSeek-R1-Distill-Qwen-14B — Best Reasoning Fit

Use this when you want step-by-step reasoning behavior without pushing a 24GB card into a 70B offload setup. Keep an eye on answer latency: reasoning models can generate longer traces, so token count can matter as much as tokens-per-second.

ollama pull deepseek-r1:14b

⚡ 4. Phi-4 14B — Compact Quality, Not 128K Context

Older versions of this page treated Phi-4 as the long-document pick. That was wrong for the current source-backed model card. Keep Phi-4 in the toolkit for compact high-quality local work, but choose Mistral Small 3.1 or a configured Qwen long-context setup for long documents.

ollama pull phi4:14b

🎨 5. Gemma 2 27B — Creative Writing Alternative

Gemma 2 27B remains a useful alternative when the output style matters and you do not need long-context retrieval. Verify the license/terms for your use case before commercial deployment because Google's Gemma terms are not the same as Apache 2.0.

ollama pull gemma2:27b

🔥 6. Mistral Small 3.1 24B — Best Long-Context 24GB Pick

Mistral Small 3.1 is the model to test when your 24GB GPU needs long documents, long chats, or larger retrieved context. It is smaller than the 32B Qwen models, which can leave more memory for KV cache when context is the limiting factor.

ollama pull mistral-small:24b

🏋️ 7. Llama 3.3 70B — The Stretch Pick

Use Llama 3.3 70B only when you have a clear reason to accept slower local generation. If 70B is your daily target, compare bigger local platforms in Best Hardware for Local LLMs and NVIDIA DGX Spark Guide.

ollama pull llama3.3:70b

Benchmark Comparison: RTX 4090 vs RTX 3090

This refresh removes the old unsourced fixed speed table. The source-backed facts are narrower: both GPUs have 24GB of VRAM, the RTX 4090 is a newer generation card, and NVIDIA lists different bandwidth/power/spec details on the official product pages (RTX 3090 specs, RTX 4090 specs). Real tokens-per-second depends on model file, quantization, context length, prompt length, CUDA/runtime version, and batching.

Quick local benchmark method: pick one prompt, one context size, one quantized model file, and run the same command on both machines. Record tokens-per-second, total latency, power draw if you measure it, and whether the model stayed fully on GPU.

Best Model by Use Case

Everyday Chat & General Purpose

→ Qwen2.5 32B at 4-bit quantization. The model card confirms the 32.5B size, Apache 2.0 license, and long-context capability with configuration caveats (source).

Coding & Development

→ Qwen2.5-Coder 32B at 4-bit quantization. The Coder model card confirms the 32.5B size and Apache 2.0 license (source). For agent workflows, also read Build Your Own AI Coding Agent.

Math, Logic & Reasoning

→ DeepSeek-R1-Distill-Qwen-14B. DeepSeek publishes the 14B distill and MIT license in the model card (source).

Long Document Processing

→ Mistral Small 3.1 24B. Mistral's model card lists 24B parameters, Apache 2.0, and a 128K context window (source).

Creative Writing & Copywriting

→ Gemma 2 27B or Qwen2.5 32B. Google documents the 27B Gemma 2 variant and Gemma terms (model card, terms); Qwen's card documents Apache 2.0 if permissive licensing is the deciding factor (source).

Speed-Critical Local Apps

→ Test 14B or 24B before forcing 32B. Smaller models leave more memory for context and overhead; verify with your own runtime because speed claims depend on the full stack.

Maximum Intelligence, Patience Required

→ Llama 3.3 70B with offload or bigger hardware. Ollama and Meta document the 70B model; a 24GB card is the compromise path, not the ideal path (Ollama, Meta).

The Recommended Toolkit

Most 24GB owners should keep a small set of models and switch by task. These commands use published Ollama library tags.

# The 24GB local-LLM toolkit
ollama pull qwen2.5:32b          # General purpose: https://ollama.com/library/qwen2.5
ollama pull qwen2.5-coder:32b    # Coding: https://ollama.com/library/qwen2.5-coder
ollama pull deepseek-r1:14b      # Reasoning: https://ollama.com/library/deepseek-r1
ollama pull phi4:14b             # Compact 14B option: https://ollama.com/library/phi4
ollama pull mistral-small:24b    # Long context: https://ollama.com/library/mistral-small

Run one heavy model at a time on a single 24GB GPU unless you have explicitly tested multi-model residency. If your work shifts toward hosted/rented GPUs, benchmark the rental box the same way you benchmark local hardware.

RTX 3090 vs RTX 4090: Which to Buy?

Do not buy either card because a generic benchmark chart says it is perfect for every 70B workload. Buy a 24GB card when your target is 14B-32B local inference and you are comfortable validating quantization/context settings yourself. For a broader buying comparison, see Best Budget GPU for Local AI: RTX 5060 Ti vs Used RTX 3090 and Best Local LLMs for RTX 4090.

Recommended Hardware

Disclosure: The Amazon links below use ToolHalla's affiliate tag (toolhalla20-20), and the Vast.ai link is a referral link. ToolHalla may earn a commission at no extra cost to you.

• NVIDIA GeForce RTX 3090 (24GB) — Good fit when the goal is 24GB VRAM at the lowest practical hardware cost; verify condition, cooling, and seller history before buying used. Check RTX 3090 listings on Amazon
• NVIDIA GeForce RTX 4090 (24GB) — Same VRAM class with newer-generation performance; choose it when speed and warranty matter more than lowest upfront cost. Check RTX 4090 listings on Amazon
• 64GB system RAM — Helpful for development tools, retrieval stacks, and CPU-offload experiments around larger models. Check 64GB DDR5 kits on Amazon
• 2TB NVMe SSD — Local model libraries grow quickly when you keep several quantized variants. Check 2TB NVMe SSDs on Amazon
• Temporary high-VRAM cloud GPU — Use a rental box before buying hardware for 70B or higher-memory experiments. Compare rentals on Vast.ai

FAQ

What is the best LLM to run on a 24GB GPU?

Start with Qwen2.5 32B for general local chat because the model card confirms the 32.5B size, Apache 2.0 license, and long-context support with configuration caveats (source). If most of your workload is code, start with Qwen2.5-Coder 32B (source).

Can an RTX 3090 or RTX 4090 run a 70B model entirely in VRAM?

Not as the normal 24GB experience. NVIDIA lists both cards at 24GB (3090, 4090), while Llama 3.3 is a 70B model (Ollama, Meta). Use offload, multi-GPU, unified memory, or rented high-VRAM hardware when 70B is the target.

Is Phi-4 the best long-document model for 24GB GPUs?

No. Microsoft's Phi-4 model card lists a 16K-token context length (source). For long documents on this GPU tier, test Mistral Small 3.1 24B because Mistral lists a 128K context window (source).

Is Qwen2.5 still worth using if newer Qwen models exist?

Yes when you need a stable, well-supported 32B local workflow today; Qwen's official model card and Ollama tag are easy to verify (model card, Ollama). If you are deciding whether to switch, read Qwen 3.5 vs Qwen 2.5: Speed, VRAM, Upgrade Call.

How much system RAM should I pair with a 24GB GPU?

Use enough RAM for the OS, development tools, retrieval/database services, and any CPU offload tests. For 70B experiments, the model class itself is the warning sign: Llama 3.3 is documented as 70B (Ollama, Meta), so extra system RAM does not turn a 24GB GPU into a full high-VRAM workstation; it only gives the runtime somewhere to offload.

Ollama or llama.cpp — which should I use?

Use Ollama first if you want the fastest path from install to a running model (Ollama download). Use llama.cpp when you need lower-level control over GGUF files and quantization (llama.cpp quantize docs). See the full comparison in Ollama vs LM Studio vs llama.cpp.

Should I rent a GPU instead of buying a 24GB card?

Rent first when you are testing 70B models, 48GB/80GB VRAM requirements, or short projects where buying hardware would be wasteful. Use the canonical ToolHalla Vast.ai referral link if you want to support the site: Vast.ai GPU rentals. If you want CPU-only experiments instead, see Microsoft BitNet: Run 100B Parameter LLMs on a Single CPU.

Sources

• NVIDIA GeForce RTX 3090 specs: https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090/
• NVIDIA GeForce RTX 4090 specs: https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
• Ollama download: https://ollama.com/download
• Ollama qwen2.5: https://ollama.com/library/qwen2.5
• Ollama qwen2.5-coder: https://ollama.com/library/qwen2.5-coder
• Ollama deepseek-r1: https://ollama.com/library/deepseek-r1
• Ollama phi4: https://ollama.com/library/phi4
• Ollama mistral-small: https://ollama.com/library/mistral-small
• Ollama llama3.3: https://ollama.com/library/llama3.3
• Qwen2.5 32B Instruct model card: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct
• Qwen2.5-Coder 32B Instruct model card: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct
• DeepSeek-R1-Distill-Qwen-14B model card: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
• Microsoft Phi-4 model card: https://huggingface.co/microsoft/phi-4
• Mistral Small 3.1 24B model card: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503
• Google Gemma 2 model card: https://ai.google.dev/gemma/docs/model_card_2
• Google Gemma terms: https://ai.google.dev/gemma/terms
• Meta Llama 3.3 docs: https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_3/
• llama.cpp quantization docs: https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md

*More local AI guides: Best Hardware for Local LLMs · Best Local LLMs for RTX 5090 · Best Budget GPU for Local AI · Best Local LLMs for RTX 4090 · Qwen 3.5 vs Qwen 2.5 · Microsoft BitNet CPU LLMs.*

*Last updated: June 20, 2026.*

If you own an RTX 3090 or RTX 4090, you own a 24GB VRAM card according to NVIDIA's official specs (RTX 3090, RTX 4090). That amount of VRAM is enough for strong 14B-32B local language models when you use quantized files, but it is not the same as a high-VRAM workstation for daily 70B use.

What to install first

Use Ollama if you want the quickest setup path (Ollama download). Then start with one of these published Ollama tags:

ollama pull qwen2.5:32b
ollama run qwen2.5:32b

Qwen2.5 32B is a good first model because Qwen's model card lists 32.5B parameters, Apache 2.0 licensing, and long-context capability with configuration caveats (model card). If you mainly write code, use Qwen2.5-Coder 32B instead (model card, Ollama tag).

Simple model picks

• General chat: Qwen2.5 32B (source)
• Coding: Qwen2.5-Coder 32B (source)
• Reasoning: DeepSeek-R1-Distill-Qwen-14B, published by DeepSeek with an MIT license (source)
• Long documents: Mistral Small 3.1 24B, whose model card lists a 128K context window and Apache 2.0 license (source)
• Compact alternative: Phi-4 14B, whose model card lists 14B parameters, 16K context, and MIT license (source)

What not to do first

Do not start with a 70B model unless you are comfortable with offload and slow tests. Llama 3.3 is documented as a 70B model by Ollama and Meta (Ollama, Meta docs). A 24GB card is better used for 14B-32B models unless your goal is specifically to experiment.

Should you buy a 3090 or 4090?

Both cards have 24GB VRAM according to NVIDIA, so they fit the same model class (3090, 4090). Choose the 3090 route when cost matters most; choose the 4090 route when speed, warranty, and newer hardware matter more. Check current listings rather than relying on old price claims.

Disclosure: Hardware links in the full guide use the ToolHalla Amazon affiliate tag (toolhalla20-20), and the Vast.ai link is a referral link.

Next reads: Best Budget GPU for Local AI and Ollama vs LM Studio vs llama.cpp.