Best LLMs for 24GB GPUs: RTX 3090 & 4090 Guide (2026)

24GB of VRAM is the sweet spot for running LLMs locally in 2026, allowing 32B parameter models at high-quality quantization — the threshold where open-source models become genuinely useful for real work.

The RTX 3090 and RTX 4090 both offer 24GB of VRAM. The 3090, priced at $500–700 used, is the best value for local AI. The 4090, priced at $1,000–1,600, is 30–40% faster. Both GPUs run the same models; the difference lies in speed, not capability.

This guide covers every model worth running on 24GB, detailing VRAM budgets, speed benchmarks, and recommendations by use case.

Quick Start

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

Three commands, and you're running one of the most capable open-source models locally. For a deeper comparison of inference frameworks, see our Ollama vs LM Studio vs llama.cpp guide.

Understanding VRAM Budgets on 24GB

Before selecting models, understand VRAM usage:

At 4K context, you have ~22–23 GB available for model weights. At 32K context, the KV cache grows, leaving 18–20 GB. Plan accordingly.

Quantization: What Fits and What Doesn't

Quantization compresses model weights to use less memory. Key formats on 24GB:

The sweet spot for 24GB: 32B models at Q4_K_M or 14B models at Q8/FP16. Avoid running 70B models at Q2 — a 32B model at Q5 often provides better output.

Top Models for 24GB GPUs (2026)

🏆 1. Qwen 2.5 32B — The All-Rounder

The daily driver for most 24GB users. Qwen 2.5 32B at Q4_K_M fits with 5 GB of headroom — enough for 8K–16K context comfortably. Performance is strong across coding, reasoning, writing, and general chat. Apache 2.0 license allows full commercial use.

At Q5_K_M (~23 GB), quality improves on nuanced reasoning and creative tasks, but context windows are shorter. Worth trying if your use case doesn't need long context.

ollama pull qwen2.5:32b

💻 2. Qwen 2.5 Coder 32B — Best for Coding

The coding-specific variant of Qwen 2.5. At Q4_K_M, it produces cleaner, more syntactically correct code than the general model — especially in Python, TypeScript, and Rust. For professional development work, the quality difference compounds across a full coding session.

If you code more than 50% of the time, use the Coder variant. Otherwise, the general model is more versatile.

For more on coding agents, see our build your own AI coding agent guide.

ollama pull qwen2.5-coder:32b

🧮 3. DeepSeek R1 Distill 14B — Best for Reasoning

DeepSeek's chain-of-thought reasoning model distilled to 14B parameters. At Q8_0 on 24GB, you get near-lossless quality with 9 GB of VRAM to spare — plenty for long context and the extended reasoning traces this model produces.

FP16 technically fits at ~28 GB with aggressive offloading, but inference slows dramatically. Stick with Q8_0 — the quality difference from FP16 is negligible on a 14B model.

This model excels in math competitions, logic puzzles, and tasks requiring step-by-step reasoning.

ollama pull deepseek-r1:14b

⚡ 4. Phi-4 14B — Best for Long Documents

Microsoft's Phi-4 at Q8_0 leaves 9 GB free — enough to push the 128K context window significantly further than most models. Load entire codebases, research papers, or book-length documents and query them interactively.

The 128K context window is the key differentiator. Most 32B models are limited to 33K. If your use case involves processing long documents, Phi-4 at Q8 is the optimal choice on 24GB.

ollama pull phi4:14b

🎨 5. Gemma 2 27B — Best for Creative Writing

Google's Gemma 2 at Q5_K_M fits tightly on 24GB with minimal headroom. At Q4_K_M, you have comfortable room. The output quality is arguably the most "human" of any open-source model — natural phrasing, fewer repetition loops, better creative range.

The 8K context window is the main limitation. For writing tasks that don't need long context (blog posts, emails, short stories), Gemma 2 is hard to beat.

ollama pull gemma2:27b

🔥 6. Mistral Small 3.1 24B — Best for Speed

Mistral's small-but-fast model fits comfortably at Q5_K_M with 4 GB to spare. Combines good general performance with 128K context and fast inference. Not the best at any single task, but the best all-around speed-to-quality ratio on 24GB.

ollama pull mistral-small:24b

🏋️ 7. Llama 3.3 70B — The Stretch Pick

You can technically load Llama 3.3 70B at Q3_K_M by offloading layers to system RAM. It works for batch processing and non-interactive tasks where you need maximum intelligence and don't mind waiting.

Be honest with yourself: if you frequently need 70B models at usable speeds, 24GB isn't enough. Consider Apple Silicon with 128GB+ or the NVIDIA DGX Spark.

ollama pull llama3.3:70b

Benchmark Comparison: RTX 4090 vs RTX 3090

All benchmarks at Q4_K_M with 4K context, single-user inference via Ollama:

*70B requires CPU offloading — speeds are approximate and depend heavily on system RAM speed.

Takeaway: The RTX 4090 is consistently 30–40% faster per token than the RTX 3090. Both run identical models. The 3090 at $500–700 used is the better value unless you're running inference all day.

Best Model by Use Case

Everyday Chat & General Purpose

→ Qwen 2.5 32B (Q4_K_M) — Versatile, strong across tasks, Apache 2.0 licensed.

Coding & Development

→ Qwen 2.5 Coder 32B (Q4_K_M) — Purpose-built for code. Pair it with Claude Code or Cursor for the ultimate local+cloud coding setup.

Math, Logic & Reasoning

→ DeepSeek R1 14B (Q8_0) — Chain-of-thought reasoning at near-perfect quality. The 14B sweet spot.

Long Document Processing

→ Phi-4 14B (Q8_0) — 128K context window with 9 GB headroom. Load entire codebases or papers.

Creative Writing & Copywriting

→ Gemma 2 27B (Q4_K_M) — Most natural-sounding output. Human-like phrasing.

Speed-Critical Applications

→ Mistral Small 3.1 24B (Q5_K_M) — Fast inference, 128K context, good all-around quality.

Maximum Intelligence (Patience Required)

→ Llama 3.3 70B (Q3_K_M + offload) — For batch processing when you need the absolute best output and can wait.

The Recommended Toolkit

Most power users keep 3–4 models downloaded and switch by task:

# The 24GB toolkit
ollama pull qwen2.5:32b          # General purpose
ollama pull qwen2.5-coder:32b    # Coding
ollama pull deepseek-r1:14b      # Reasoning (Q8)
ollama pull phi4:14b              # Long documents (Q8, 128K)
ollama pull mistral-nemo:12b      # Quick Q&A (fastest)

Total disk space: ~80 GB. You can only run one at a time on 24GB VRAM, but switching takes seconds with Ollama.

RTX 3090 vs RTX 4090: Which to Buy?

Our recommendation: Buy a used RTX 3090 unless you run inference for hours daily. The 35% speed difference doesn't justify 2–3× the price for most people. The saved money is better spent on RAM (64 GB recommended) or fast NVMe storage.

If you want more than 24GB, the RTX 5090 with 32GB — which unlocks 32B models at Q5_K_M and 14B at full FP16.

Recommended Hardware

NVIDIA RTX 3090 Founders Edition (24 GB) — The undisputed value king for local LLMs. 24GB GDDR6X runs every 32B model at Q4_K_M. Used prices make this the best performance-per-dollar in the market.

→ Check price on Amazon

NVIDIA RTX 4090 Founders Edition (24 GB) — Same 24GB VRAM but 30–40% faster inference. Worth it if you run models all day and value speed over savings.

→ Check price on Amazon

Samsung 990 Pro NVMe SSD (2 TB) — Fast storage for your model library. 7,450 MB/s reads keep model loading snappy when switching between models.

→ Check price on Amazon

Corsair Vengeance DDR5-6000 (64 GB, 2×32 GB) — 64GB system RAM is essential for CPU offloading when stretching into 70B territory. Also keeps Docker and other tools running smoothly alongside inference.

→ Check price on Amazon

*Disclosure: Links above are affiliate links. ToolHalla may earn a commission at no extra cost to you. We only recommend hardware we'd actually use.*

FAQ

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

A: Qwen 2.5 32B at Q4_K_M is the sweet spot — it fits comfortably while delivering strong general-purpose performance. For coding, Qwen 2.5 Coder 32B at Q4 is the best option. For reasoning, DeepSeek R1 14B at Q8_0 gives near-perfect quality with headroom to spare.

Q: Is the RTX 3090 still worth buying for local AI in 2026?

A: Absolutely. The RTX 3090 remains the best value-per-VRAM-dollar in local AI. At $500–700 used, it gives you the same 24GB VRAM as the RTX 4090 and runs the same models — just 30–40% slower. For most people, the savings far outweigh the speed difference.

Q: Can I run 70B parameter models on a 24GB GPU?

A: Not entirely in VRAM. You can partially offload a 70B model to system RAM, but inference will be very slow (2–6 tok/s). For usable 70B performance, you need 48GB+ VRAM or a unified memory system like Apple Silicon with 128GB+ or the NVIDIA DGX Spark.

Q: What's the difference between Q4_K_M, Q5_K_M, and Q8_0 quantization?

A: These refer to the bit-depth of model weight compression. Q4_K_M uses ~4 bits per weight (smallest, some quality loss), Q5_K_M uses ~5 bits (excellent quality, slightly larger), and Q8_0 uses 8 bits (near-lossless). On 24GB GPUs, Q4_K_M lets you fit larger models; Q5 or Q8 gives better quality on smaller ones.

Q: How much system RAM do I need alongside a 24GB GPU?

A: 32GB is the minimum for comfortable local LLM work. 64GB is recommended — it gives you headroom for CPU offloading (running part of a model in system RAM) and keeps the OS, Docker, and other tools running smoothly alongside inference.

Q: Ollama or llama.cpp — which should I use?

A: Ollama is the easiest way to get started — one command to install, one command to run. llama.cpp gives you more control over quantization, context sizes, and advanced features like speculative decoding. Most people should start with Ollama and move to llama.cpp if they need fine-grained tuning. See our full comparison.

Q: Does the RTX 5090 (32GB) make the 3090/4090 obsolete?

A: Not for everyone. The RTX 5090's 32GB unlocks 32B models at Q5_K_M and 14B at full FP16 — quality levels 24GB cards can't match. But at ~$2,000, it's 3–4× the price of a used 3090. If your models fit comfortably in 24GB (and most daily-use models do), the 3090 is still the better value.

*Find the perfect model for your GPU at ToolHalla.ai/models — filter by VRAM and use case.*

*More local AI guides: Best Hardware for Local LLMs · RTX 5090 Model Guide · Ollama vs LM Studio vs llama.cpp · BitNet: LLMs on CPU*

*Last updated: March 2026*

Recommended Hardware

Recommended Products

• NVIDIA GeForce RTX 3090 — The RTX 3090 offers excellent performance and 24GB of VRAM, making it a great choice for running large language models locally.
• NVIDIA GeForce RTX 4090 — For those needing even more speed, the RTX 4090 provides a significant performance boost while maintaining the 24GB VRAM necessary for running 32B parameter models.
• Fractal Design Meshify C — A high-quality case that can accommodate powerful GPUs like the RTX 3090 and 4090, ensuring proper cooling and airflow for optimal performance.

You've got 24 GB of VRAM — an RTX 3090, RTX 4090, or RTX 5090. That's a serious amount of GPU memory. Now the real question: which AI models actually take advantage of it?

Quick answer: With 24 GB VRAM, you can run 70B-parameter models at lower quality, or 32–34B models at high quality. That puts you in the top tier of consumer-grade local AI.

What does "24 GB VRAM" mean for AI? Think of VRAM like a desk. The bigger the desk, the larger the AI model you can spread out and work with. 24 GB is a large desk — most consumer cards have 8–16 GB. You can load models that simply won't fit on cheaper cards.

What "Billion Parameters" Actually Means

AI models are described by their parameter count — 7B, 13B, 34B, 70B. More parameters generally means smarter, more capable AI. But more parameters also means more VRAM required.

A rough guide for 24 GB VRAM:

• 7B models (Q8 quality): Runs easily, barely uses 10 GB. Best for speed.
• 13–14B models (Q8): Fits comfortably, great balance of quality and speed.
• 32–34B models (Q6/Q8): Fits in 24 GB — this is the sweet spot for 24 GB owners.
• 70B models (Q4): Just fits in 24 GB at reduced quality. Slower but capable.

What is "Q8" or "Q4"? These are quantization levels — they control how much each parameter is compressed to save memory. Q8 is near-original quality. Q4 uses half the memory but with some quality loss. Q2 is the smallest/fastest but noticeably degraded.

The Best Models for Your 24 GB Card

For Coding: Qwen2.5-Coder-32B (Q6)

Alibaba's Qwen Coder 32B runs beautifully in 24 GB. It beats GPT-4o on many coding benchmarks and is the top pick for anyone writing code with local AI. Load it in Ollama with ollama run qwen2.5-coder:32b.

For General Chat: Llama 3.3 70B (Q4_K_M)

Meta's Llama 3.3 70B squeezed into Q4 quantization fits in 24 GB. It's remarkably smart for a model that runs on consumer hardware. Best for complex reasoning, long documents, creative writing.

For Speed + Quality: Mistral Small 3.1 (24B)

Mistral's 24B model fits perfectly in 24 GB at full quality. Blazing fast and surprisingly capable — great for agentic workflows where you need rapid responses.

For Math and Reasoning: DeepSeek-R1-Distill-Qwen-32B

DeepSeek's distilled reasoning model at 32B is exceptional for step-by-step problem solving, math, and logic tasks. A genuine GPT-o1 competitor that runs locally.

For Multimodal (Text + Images): LLaVA 34B

If you need to analyze images as well as text, LLaVA 34B in Q6 fits in 24 GB and handles visual tasks well.

Quick Verdict

How to Download and Run These Models

The easiest path is Ollama:

1. Install from ollama.ai

2. Open terminal, type: ollama run qwen2.5-coder:32b

3. Ollama downloads the model and starts a chat session

For a graphical interface, use LM Studio — download models from its built-in model browser.

Will These Models Saturate Your GPU?

Yes. A 24 GB card (especially RTX 4090 or 5090) has enough memory bandwidth to run 32B models at 20–40 tokens per second — fast enough for real-time conversation. The RTX 3090 is slightly slower but still very usable.

Bottom Line

24 GB VRAM is currently the best consumer-grade sweet spot for local AI. You can run:

• Coding models that rival Claude Sonnet
• General-purpose models that approach GPT-4 quality
• The latest reasoning models (DeepSeek R1)

The RTX 3090 (used, ~€400) and RTX 4090 (new, ~€1800) are the two most popular choices. Both give you the same 24 GB VRAM — the 4090 is roughly twice as fast.

Start with Qwen2.5-Coder-32B for coding tasks, and Llama 3.3 70B Q4 for everything else. You'll be surprised how capable local AI has become.