What Can 10GB VRAM Run? Local AI at the 10GB Tier

Model fit at 10GB VRAM

The table below applies to any GPU with 10GB of VRAM. Individual cards differ in token generation speed (memory bandwidth) and in how easy they are to set up, but the model ceiling is the same.

Use the Local LLM Compatibility Checker to match specific models against your exact hardware configuration and workflow.

What 10GB VRAM does well

• 7B at Q8: The best quality-per-effort choice at this tier. Q8 is very close to FP16 for most tasks and fits with a workable margin. Ideal for chat, summarisation, and general coding assistance.
• 8B at Q5_K_M or Q6_K: Slightly lower precision than Q8 but leaves more room for context, which matters on a 10GB budget. A good default for Llama 3 8B and Gemma 3 8B.
• 4B at FP16: Full precision on a small model, very fast. Excellent for autocomplete, tool-call loops, and low-latency agents where speed beats depth.

Where 10GB gets tight: 13B–14B and context headroom

A 13B model at Q4_K_M needs about 7.9 GB and a 14B model about 9 GB. Those weights fit inside 10GB — but that is only half the VRAM story. Every token in your context window is stored in the KV cache, which sits in VRAM alongside the weights and grows as the conversation or document gets longer.

With a 14B model loaded, roughly 1 GB of VRAM remains — and measurement says that is not enough even at short context. On an RTX 3080 at 4K tokens, 10–13% of the model was already resident in system RAM rather than VRAM, and Phi-4 generated 22.9 tokens per second, about 27% of what the memory bandwidth allows. The runtime does not refuse the context; it quietly spills layers and slows down, which is easy to mistake for the model simply being slow. On a 12GB card the same model keeps its working set on the card — the difference between "runs, but offloading" and "comfortable for real work." This is the core reason 10GB is an existing-owner tier rather than a recommended new purchase.

• Keep context modest: 4K–8K context keeps 13B–14B Q4 fully on-GPU. Push past that and headroom disappears.
• Prefer a smaller model with room to breathe: a 7B Q8 at a long context often beats a 14B Q4 forced into a cramped one.
• Watch the KV cache, not just the weights: the weight estimate is the floor, not the total VRAM budget.

CPU and RAM offload: possible, but not a fix

Runtimes like Ollama and llama.cpp can offload the layers that do not fit in VRAM to system RAM, where the CPU processes them. This lets a 10GB card technically "run" a 30B model, but at a heavy speed cost — offloaded inference is typically several times slower than a fully GPU-resident model, and not comfortable for interactive chat. Fast DDR5 and plenty of it soften the penalty but do not remove it. Treat offload as a way to experiment with a larger model occasionally, not as your daily driver. For interactive use, stay within what fits in the 10GB pool.

Representative 10GB GPUs and their runtime paths

Three current cards land at 10GB across the three GPU vendors. They share the 10GB model ceiling but not the runtime experience — this is the most important practical difference between them for local AI.

• NVIDIA CUDA (RTX 3080 10GB): The most mature local AI path. Ollama, LM Studio, llama.cpp, and vLLM all detect CUDA automatically. If you want the least setup friction at 10GB, this is it. See the dedicated RTX 3080 10GB guide.
• AMD ROCm / Vulkan (RX 6700 10GB): Works, but expect more setup. Official ROCm support for RDNA 2 consumer cards has always been narrower than NVIDIA CUDA, and the Vulkan backend in llama.cpp is frequently the more dependable route on these cards. Linux is generally the better-tested platform for AMD local AI.
• Intel oneAPI / SYCL / IPEX-LLM / Vulkan (Arc B570): A newer, faster-moving stack. Intel's IPEX-LLM and the SYCL and Vulkan backends can run 7B–8B models on Arc, but driver and runtime maturity trails the NVIDIA path. Check current runtime notes before committing to Arc for a local AI workload.

Recommended runtimes for the 10GB tier

• Ollama — The easiest start on NVIDIA and, increasingly, on AMD. Pull a 7B or 8B model and it detects your GPU automatically.
• LM Studio — Desktop GUI for downloading and testing quantizations without a terminal. Good for finding the Q5/Q6/Q8 balance that fits your 10GB budget.
• llama.cpp: The underlying engine. Its Vulkan backend is especially useful on AMD and Intel cards where CUDA is not an option, and it gives fine control over GPU layers and context.
• Open WebUI — A private browser chat interface over Ollama. Runs via Docker for a clean local workspace with no external dependencies.

Recommended models for 10GB VRAM

• Qwen 3 8B at Q5_K_M or Q6_K — daily driver: A strong general model that leaves context headroom at 10GB. Try ollama pull qwen3:8b-q5_K_M and step up to Q6/Q8 if your context needs are modest.
• Qwen 3 8B / Llama 3.1 8B at Q8 — maximum quality: Use when you want the best 8B output and can keep context short. Fits with little to spare.
• Qwen 2.5 14B Q4_K_M — depth, short context: ollama pull qwen2.5:14b. More model knowledge at reduced precision, but only about 1 GB of VRAM is left after the weights load — measured on an RTX 3080, part of the model spills to system RAM even at short context, so expect roughly 23 tokens per second rather than the 40-plus a fully resident model gives you.
• Gemma 3 4B FP16 — fast and precise: Full precision on a small model. Ideal for autocomplete, tools, and lightweight agents where latency matters most.

Getting started on a 10GB GPU

Start with a 7B or 8B model to confirm your GPU acceleration works, then tune the quantization to balance quality against context headroom:

# Install Ollama (Linux/macOS)
curl -fsSL https://ollama.com/install.sh | sh

# Pull an 8B model at default Q4 first (fast download, confirms GPU acceleration)
ollama pull qwen3:8b

# Balance quality vs context headroom on 10GB — Q5_K_M leaves more room for KV cache
ollama pull qwen3:8b-q5_K_M
ollama run qwen3:8b-q5_K_M

# Depth option: 14B at Q4 — keep the context window short at this tier
ollama pull qwen2.5:14b
ollama run qwen2.5:14b

Upgrade paths from 10GB

• 12GB (tier guide): The natural next step and the recommended floor for a new purchase. The extra 2 GB turns "13B–14B Q4 with short context" into comfortable everyday use. RTX 3060 12GB, RTX 4070, and RTX 5070 all land here.
• 16GB (tier guide): The quality-upgrade tier — 7B at FP16 and 13B–14B at Q8. Meaningfully better output on the larger models, plus real context headroom.
• 24GB (tier guide): The consumer sweet spot for serious local AI — 30B at Q4, 14B at Q8, and 7B at full precision. RTX 3090 (used) and RTX 4090 sit here.

Cloud GPU fallback

A 10GB card is well suited to private assistants, coding help, and document chat. When a workflow needs 30B or 70B models, longer context than the 10GB budget allows, or higher concurrency, renting a larger cloud GPU is the practical fallback — often cheaper than upgrading for occasional large-model work. See the tools directory for managed GPU options and the AI PC Builder if you are planning a new machine instead.

Related hardware

FAQ

Disclosure

OpenSourcesAI produces hardware assessments independently. Hardware specifications are sourced from manufacturer documentation. Model VRAM estimates are derived from GGUF quantization formulas and may vary across runtime versions, context lengths, and model architectures. Verify before making purchasing decisions.

Last verified: July 2026