How Much VRAM Do You Need to Run an Open-Source Coding Model With Cline?

For a usable local Cline setup, 16GB of VRAM is the practical starting point, 24GB is the sweet spot, and 48GB makes larger coding models and agent-sized context windows much less painful. Don’t size your GPU from a model’s parameter count alone: the downloaded weights, KV cache, runtime overhead, and your desired context length all compete for the same memory.

If you only want autocomplete-like help, an 8GB card can run a small quantized coding model. If you want Cline to inspect files, call tools, recover from failed edits, and stay coherent through a multi-step task, 12GB is a compromise and 16GB is where local use starts to feel reasonable.

The short buying guide

• 8GB VRAM: Run small 3B–7B models at 4-bit quantization. Fine for focused questions and short edits; expect to keep tasks narrow and context small.
• 12GB VRAM: Comfortable for many 7B–14B-class 4-bit models, but you’ll need to watch context length. A decent low-cost entry point, not an agent workstation.
• 16GB VRAM: The practical minimum for daily Cline use. Run a good small model fully on GPU and leave enough room for roughly 8K–16K context, depending on its architecture.
• 24GB VRAM: The best all-around target. It makes 14B–32B quantized models realistic, including 30B mixture-of-experts models at modest context sizes.
• 48GB VRAM: Buy this when repo-scale context, higher-quality quantization, or a larger model matters more than price. It is not automatically enough for every advertised 128K or 256K context window.
• 80GB+ VRAM: The territory for big models, generous contexts, multiple concurrent requests, or avoiding tradeoffs. It’s great, but not required to make Cline useful.

Why Cline changes the calculation

A chat model can appear to fit during a quick one-paragraph prompt and then bog down when you use it as an agent. Cline sends more than your last instruction: system guidance, tool definitions, file contents, command output, diffs, conversation history, and responses from tools all occupy the context window. It can compact older material, and its local-model documentation recommends enabling Compact Prompt, keeping tasks focused, and starting a new task once context grows. Those are memory-management techniques as much as prompting advice.

That matters because the KV cache grows with tokens processed. The cache holds attention state so the model does not recompute every earlier token before generating the next one. It is allocated separately from model weights. A GPU that fits a model file exactly is not a GPU that can use that model well with Cline.

Ollama makes this visible in an unusually useful way. Its current defaults assign 4K context below 24 GiB of VRAM, 32K from 24–48 GiB, and 256K at 48 GiB or more. Those are defaults, not a promise that every model will remain fully GPU-resident at that context. Ollama explicitly notes that increasing context requires more memory, and ollama ps shows whether a loaded model is 100% GPU or split between CPU and GPU.

A worked example: Qwen3-Coder 30B

Qwen3-Coder 30B is a useful example because its name invites the wrong intuition. It is a mixture-of-experts model with 30.5B total parameters but only 3.3B activated per token. The low active count helps compute efficiency; it does not mean you only need memory for 3.3B parameters. The unused experts still have to be stored.

Ollama’s standard qwen3-coder:30b download is about 19GB and uses Q4_K_M quantization. So a 16GB GPU cannot hold the weights entirely in VRAM before we even account for context or runtime buffers. It may still run through CPU/GPU offload if you have enough system RAM, but prompt ingestion and tool-heavy agent loops will feel noticeably slower.

On a 24GB card, that same 19GB model is plausible for Cline, but it is a tight configuration rather than unlimited headroom. Start around 8K or 16K context, run one request at a time, and check the actual placement. At 32K context, the KV cache and working buffers can make full GPU residency dependent on the runtime, cache type, and what else is using the card. A 48GB card provides much more breathing room for this model at 32K context.

The model advertises native 256K context, but that is a capability limit, not a hardware recommendation. For its published configuration—48 layers, four KV heads, 128-dimensional keys and values—a conventional FP16 KV cache is roughly 96 KiB per token. At 32K tokens that is about 3 GiB; at 256K it is about 24 GiB, before runtime overhead. A 19GB quantized model plus that cache is already near 43GB without leaving much safety margin. This is why “supports 256K” and “runs comfortably at 256K on my GPU” are completely different statements.

Estimate VRAM before downloading anything

Use this back-of-the-napkin rule: take the actual quantized model file size, add expected KV-cache memory for your context, then reserve another 15–25% for runtime overhead and display/other GPU use. For a small 7B coding model in a 4-bit format, the weights may be around 4–6GB, so 8GB can work at short context while 12GB–16GB is much nicer. For a 14B model, expect roughly 8–10GB of weights at a comparable quantization; 16GB is a sensible target. For a 30B model at 4-bit, expect roughly 17–20GB of weights, making 24GB the realistic floor for a fully GPU-loaded setup.

File size is an excellent first estimate because it represents the stored weights you must load. It is not a complete memory estimate. Quantization labels are also not exact promises: Q4_K_M is a mixed tensor recipe, and different architectures have different non-weight memory needs.

Set up Cline without guessing

Start with a deliberately small context, prove the model is on the GPU, then increase context only if your tasks require it. With Ollama, pull and launch the model, choose Ollama as Cline’s provider, set the local base URL, select the model, and turn on Cline’s Compact Prompt feature.

ollama pull qwen3-coder:30b
ollama run qwen3-coder:30b
ollama ps

In the ollama ps output, look at both PROCESSOR and CONTEXT. 100% GPU is your goal. A CPU/GPU percentage split is not a failure, but it tells you that either weights or context spilled into system memory. If the agent becomes slow after reading several files, reduce the context length before blaming the model.

For Ollama, you can set a server-wide context target before starting the service. Begin modestly rather than copying a model’s maximum advertised number. For a 16GB GPU, 8192 is a sensible first experiment; for 24GB, try 16384 and measure; for 48GB, 32768 is a sensible starting place for a 30B 4-bit coding model.

OLLAMA_CONTEXT_LENGTH=16384 ollama serve

One last practical rule: spend VRAM on the workflow bottleneck. If Cline loses the thread, increase context or start fresh tasks. If it makes weak edits, move up a model class or quantization quality. If it is painfully slow, prioritize keeping all weights on GPU rather than chasing an enormous context number. For most software engineers, a 24GB GPU paired with a 4-bit 14B–30B coding model and disciplined task scope is the local setup that finally feels like a tool instead of a demo.