What Can 24GB VRAM Run? Local AI at the 24GB Tier

Model fit at 24GB VRAM

The table below applies to any GPU with 24GB of dedicated VRAM. Bandwidth differences affect tokens-per-second, not model fit.

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

What 24GB VRAM unlocks over 12GB

• 7B and 8B at FP16: The most important unlock. Full precision means no quantization quality tradeoff — effectively the same as the model at training time. 7B FP16 uses ~14GB with 10GB of headroom; 8B FP16 uses ~16GB with 8GB of headroom.
• 13B and 14B at Q8: On 12GB cards, these models require Q4. On 24GB, they run at Q8 — very close to FP16 quality. A meaningful improvement for instruction following, coding, and reasoning tasks.
• 30B–32B at Q4: A 32B model at Q4_K_M uses approximately 20GB and fits with 4GB headroom. This model class requires CPU offload on 12GB cards and runs at interactive speed on 24GB GPUs.

What 24GB VRAM still cannot do

• 70B models at usable quality: Q4 on 70B needs ~38–42GB. Even at Q2 (~23GB), quality degrades significantly. 70B at Q4 requires Apple Silicon 64GB unified memory, a dual-RTX-3090 NVLink setup, or cloud inference.
• 13B or 14B at FP16: FP16 on 13B needs ~26GB; FP16 on 14B needs ~28GB. Both exceed 24GB. Q8 is the practical maximum quality for 13B+ at this tier.
• Multi-GPU VRAM pooling (RTX 4090 only): The RTX 4090 has no NVLink support. Two 4090s in the same machine give 48GB total VRAM but cannot be combined for a single model without explicit tensor parallelism in the serving runtime.

GPUs at the 24GB tier: architecture constraints

• RTX 4090 (Ada Lovelace): NVIDIA's current consumer architecture. Ada Lovelace brings improved CUDA cores, better tensor core efficiency, and higher memory bandwidth than the prior Ampere generation. The 4090's 1008 GB/s bandwidth is the practical ceiling for consumer local AI speed. No NVLink: two 4090s cannot be bridged into a 48GB pool for a single model without explicit tensor parallelism.
• RTX 3090 (Ampere): The previous-generation consumer flagship. The 8% bandwidth gap versus the 4090 is noticeable but not disqualifying — the model ceiling is identical. Ampere is the last NVIDIA consumer generation to support NVLink 3.0, which means a dual-3090 setup with vLLM can present a unified 48GB pool. Strong used-market value.

Both cards run Ollama, LM Studio, llama.cpp, vLLM, and TGI on Linux and Windows without modification. CUDA version requirements differ slightly — verify your runtime's minimum CUDA version before upgrading drivers.

24GB vs Apple Silicon 64GB

Apple Silicon with 64GB of unified memory is the only consumer alternative that changes the model ceiling beyond 32B.

If 70B models or 14B FP16 are hard requirements, Apple Silicon 64GB changes the ceiling. If fast inference on 7B–32B models is the priority, a 24GB NVIDIA card is faster for those sizes. See the Apple Silicon guide for the full Metal-backend comparison.

Recommended models for 24GB VRAM

• Qwen 3 8B FP16 — daily driver at full precision: ollama pull qwen3:8b-fp16. The primary 24GB unlock over 12GB. Fast, high quality, zero quantization compromise.
• Qwen 2.5 14B Q8 — near-lossless 14B quality: ollama pull qwen2.5:14b-q8_0. Approximately 15GB VRAM, leaves 9GB of headroom. Strong reasoning and instruction following, very close to FP16 quality.
• Qwen 2.5 32B Q4 — the high-end local ceiling: ollama pull qwen2.5:32b. Approximately 20GB VRAM. The model class that 12GB users cannot reach without slow CPU offload.
• DeepSeek-R1 Distill Qwen 14B Q8 — reasoning at near-FP16 quality: ollama pull deepseek-r1:14b. Multi-step reasoning distilled into a 14B model. Fits in ~15GB with strong chain-of-thought capability.

Getting started: first setup at 24GB

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

# 8B at FP16 — the primary unlock vs 12GB
ollama pull qwen3:8b-fp16
ollama run qwen3:8b-fp16

# 14B at Q8 — near-lossless quality with 9 GB to spare
ollama pull qwen2.5:14b-q8_0
ollama run qwen2.5:14b-q8_0

# The high-end ceiling: 32B at Q4 (~20 GB VRAM)
ollama pull qwen3:32b
ollama run qwen3:32b

When to upgrade beyond 24GB

The jump to the next meaningful tier (48GB+) is large in cost and complexity. Worth considering only when:

• 70B models at Q4 quality are a regular requirement — not occasional curiosity.
• 13B+ at FP16 is a hard quality requirement — and Q8 quality is not acceptable.
• Multi-user serving needs 32B+ models — requiring workstation GPUs (RTX A6000 48GB) or a multi-GPU node.

For most local AI workflows through 32B models, 24GB is the practical consumer ceiling that does not need upgrading.

Cloud GPU fallback

The 24GB ceiling appears when you need 70B models at higher precision, large MoE models, multi-user serving, or long-context workloads that exhaust the KV cache headroom. Cloud GPU is the fastest path to those workloads without a hardware upgrade.

Related hardware

FAQ

Disclosure

OpenSourcesAI may earn a commission or referral fee from links to cloud GPU providers or partner tools on this page. Editorial assessments are produced independently and are not influenced by commercial relationships. Hardware specs are sourced from manufacturer documentation. Model VRAM estimates are derived from GGUF quantization formulas and may vary across runtime versions and model architectures. Verify before making purchasing decisions.

Last verified: June 2026