NVIDIA · 24GB VRAM
A GeForce RTX 4090 has 24GB of VRAM. Checked against 225 models from the Ollama library, 193 have good/excellent reference fits on the GPU at 4K context and 15 more run with part of the model in system RAM.
193
run well
15
with trade-offs
225
builds evaluated
The 24 GB desktop RTX 4090 has the same nominal capacity as the desktop RTX 3090. Larger context and higher-bit quantization can use the headroom; this page does not measure a speed advantage.
ollama ps. Intel Macs use the Ollama CPU path; Apple Silicon uses its integrated Metal GPU.Hardware specifications · Ollama support table · reviewed 2026-09-15.
Compare GeForce RTX 3090 (24 GB). The same capacity can give the same fit result; runtime support and measured speed are separate questions.
Editorial examples chosen for their documented tasks, all Q4_K_M at 4,096 tokens. Check the verdict before downloading. These are estimates, not benchmark rankings.
A small instruction-tuned model for trying local chat with more memory headroom. Review summaries against the source text.
Task and build source · Catalog checked 2026-10-13
Est. memory 2.9 GB · medium confidence
Model fully fits in usable GPU VRAM.
ollama run llama3.2:3bAn 8B instruction-tuned alternative to compare on your own prompts. It needs more memory than the 3B example and can still give incorrect answers.
Task and build source · Catalog checked 2026-10-13
Est. memory 5.8 GB · medium confidence
Model fully fits in usable GPU VRAM.
ollama run llama3.1:8bA code-focused instruct build. Review generated changes and run your tests; a memory fit is not evidence that its code is correct.
Task and build source · Catalog checked 2026-10-13
Est. memory 5.3 GB · medium confidence
Model fully fits in usable GPU VRAM.
ollama run qwen2.5-coder:7bA 14B instruct build for comparing answers when memory allows. Larger size does not establish better results for every task.
Task and build source · Catalog checked 2026-10-13
Est. memory 10.2 GB · medium confidence
Model fully fits in usable GPU VRAM.
ollama run qwen2.5:14bAfter opening Ollama's prompt, enter /set parameter num_ctx 4096 to match this calculation, within the model's supported maximum. Install a current Ollama release that supports the build. Setup and troubleshooting.
Largest first — these have good/excellent reference fits with 24GB of VRAM at 4K context. A good fit can include a small estimated CPU offload. These are technical defaults, not a quality ranking.
| Model | Build | Memory needed | Verdict |
|---|---|---|---|
| codeboogaOllama Library | codebooga:34b-v0.1-q4_K_MQ4_K_M | ~21.5 GB | Good fit |
| phind-codellamaMicrosoft | phind-codellama:34b-python-q4_K_MQ4_K_M | ~21.5 GB | Good fit |
| qwqAlibaba Cloud | qwq:32b-preview-q4_K_MQ4_K_M | ~21.4 GB | Good fit |
| olmo-3.1Allen Institute for AI | olmo-3.1:32bQ4_K_M | ~21.0 GB | Good fit |
| command-rCohere | command-r:35b-08-2024-q4_K_MQ4_K_M | ~20.9 GB | Good fit |
| qwen3-coderAlibaba Cloud | qwen3-coder:30bQ4_K_M | ~19.4 GB | Good fit |
| clefOllama Library | clef:27bQ4_K_M | ~18.5 GB | Good fit |
| qwen3.8Alibaba Cloud | qwen3.8:27bQ4_K_M | ~18.3 GB | Good fit |
| muse-glimmerOllama Library | muse-glimmer:30bQ4_K_M | ~17.4 GB | Excellent fit |
| mistral-small3.1Mistral AI | mistral-small3.1:24bQ4_K_M | ~16.6 GB | Excellent fit |
| devstral-small-2Mistral AI | devstral-small-2:24bQ4_K_M | ~16.3 GB | Excellent fit |
| mistral-small3.2Mistral AI | mistral-small3.2:24bQ4_K_M | ~16.3 GB | Excellent fit |
| lfm2Ollama Library | lfm2:24bQ4_K_M | ~16.1 GB | Excellent fit |
| devstralMistral AI | devstral:24bQ4_K_M | ~15.4 GB | Excellent fit |
| magistralMistral AI | magistral:24bQ4_K_M | ~15.4 GB | Excellent fit |
| solar-proUpstage | solar-pro:22bQ4_K_M | ~15.0 GB | Excellent fit |
| mistral-smallMistral AI | mistral-small:22b-instruct-2409-q4_K_MQ4_K_M | ~14.7 GB | Excellent fit |
| codestralMistral AI | codestral:22b-v0.1-q4_K_MQ4_K_M | ~14.7 GB | Excellent fit |
| gpt-ossOpenAI | gpt-oss:20bMXFP4 | ~14.5 GB | Excellent fit |
| gpt-oss-safeguardOpenAI | gpt-oss-safeguard:20bMXFP4 | ~14.5 GB | Excellent fit |
| phi4-reasoningMicrosoft | phi4-reasoning:14bQ4_K_M | ~12.4 GB | Excellent fit |
| deepseek-coder-v2DeepSeek AI | deepseek-coder-v2:16b-lite-base-q4_K_MQ4_K_M | ~11.9 GB | Excellent fit |
| deepseek-v2DeepSeek AI | deepseek-v2:16b-lite-chat-q4_K_MQ4_K_M | ~11.9 GB | Excellent fit |
| everythinglmOllama Library | everythinglm:13b-16k-q4_K_MQ4_K_M | ~11.4 GB | Excellent fit |
| nexusravenOllama Library | nexusraven:13b-q4_K_MQ4_K_M | ~11.4 GB | Excellent fit |
Showing the 25 largest of 193 models that run well.
Each model is evaluated at 4K context against 24GB of VRAM, counting model weights, KV cache, compute buffers and runtime overhead, minus a reserve for the display and operating system. Download sizes come from the official Ollama registry.
Reference scenario: Windows 11, 32GB of system RAM, an illustrative 8-core/16-thread CPU and 500GB free disk. Available RAM, driver version and free VRAM are unknown; the engine applies reserves. Supported GPU drivers/backends are assumed installed. Your own machine may differ — RAM, free disk space and whether an acceleration backend is actually installed all change the answer, which is what the PC scan measures directly.