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There is no single best local coding model for every GPU: the right choice depends on total model weights, quantization, context length, runtime overhead, and whether you allow system-RAM offload. For a 16GB graphics card, smaller Qwen2.5-Coder variants are the more practical starting point; a 30B-class Q4 model is estimated to need about 20GB of VRAM at minimum, so it is not a straightforward fit. Treat all capacity guidance below as planning, not a guarantee for your particular inference setup.
How to judge whether a coding model fits
Model size in parameters is only a first filter. The model’s stored weights consume memory, and quantization changes how much memory those weights use. Inference also needs room for the key-value (KV) cache as context grows, plus runtime overhead and any other GPU workloads. A model that loads at a short context may run out of memory at a longer one.
- Check total parameters and quantization. For mixture-of-experts (MoE) models, active parameters are the portion used for a token; they are not the full stored model. Total parameters remain relevant to weight memory.
- Account for context and overhead. A model’s maximum context specification does not mean that context will fit alongside its weights on your GPU.
- Distinguish VRAM from system RAM. CPU layer offload can let a runtime place some model data in system RAM when VRAM is limited, but that is a different configuration and can affect performance. LocalVRAM says its estimates may account for CPU spill, particularly with long context.
- Verify the exact build. Quantization file, inference runtime, context setting, and other GPU use all affect the result.
What fits in 8GB, 16GB, and 24GB of VRAM?
The table is a decision guide, not a compatibility guarantee. The only explicit VRAM fit figures here are a third-party estimate for one Qwen3-Coder quantization; the other recommendations are cautious comparisons based on model sizes, not published minimum-VRAM requirements.
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|---|---|---|
| 8GB | Smaller Qwen2.5-Coder variants, such as 3B; consider 7B only with careful quantization and context choices. | Qwen’s model card lists 0.5B, 1.5B, 3B, 7B, 14B, and 32B variants, but does not establish universal VRAM minimums. Fit depends on the specific quantization, runtime, and context. Qwen2.5-Coder model card |
| 16GB | Qwen2.5-Coder 7B is a reasonable starting point; 14B may be possible with a suitably small quantization and restrained context, but is not guaranteed. | The model card lists these model sizes and context support up to 128K tokens, not a guaranteed fit on 16GB. DeepSeek-Coder-V2-Lite is 16B total / 2.4B active, so its low active count alone does not prove it fits. The cited card does not give a universal consumer-GPU minimum for Lite. Qwen model card; DeepSeek-Coder-V2 model card |
| 24GB | Qwen3-Coder 30B Q4 is a possibility only with limited headroom; smaller Qwen2.5-Coder options offer more room for context and runtime needs. | LocalVRAM estimates 20GB minimum and 22GB optimal VRAM for Qwen3-Coder 30B Q4, with 32GB or more system RAM. This is a third-party estimate, not an official requirement or a test for this article; a 24GB card leaves limited margin, especially at longer contexts. LocalVRAM coding-model estimates |
For a tighter budget, start with a smaller model and increase context only after confirming stable memory use in your runtime. If you rely on offload, account for system RAM as well as VRAM and expect a different performance profile than an all-GPU run.
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Which models make sense for local coding?
Qwen2.5-Coder: a range of sizes for different budgets
Qwen lists six Qwen2.5-Coder sizes: 0.5B, 1.5B, 3B, 7B, 14B, and 32B parameters. Its model card describes context support up to 128K tokens. That breadth makes the family useful for matching a model size to available hardware, but the card does not promise that any one variant will fit a particular amount of VRAM at every quantization and context setting. The 7B and 14B variants are practical comparison points for tighter memory budgets; the 32B variant requires substantially more weight memory. Qwen2.5-Coder model card
DeepSeek-Coder-V2-Lite: active parameters are not a memory shortcut
DeepSeek AI lists Lite as 16B total parameters and 2.4B active parameters, with 128K context. The active figure describes the portion engaged per token in this MoE model; it does not reduce the total stored weights to 2.4B. The model card includes BF16 inference code for Lite but does not state a single consumer-GPU VRAM floor in the cited material. Assess a particular quantized build and runtime rather than treating the active count as a fit estimate. DeepSeek-Coder-V2 model card
DeepSeek-Coder-V2 full: a server-scale BF16 configuration
The full model is listed at 236B total parameters and 21B active parameters, with 128K context. DeepSeek AI states, “If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.” That is the model card’s guidance for BF16 full-model inference, not a requirement for every quantized community build or runtime. It describes a multi-GPU server setup, not a typical single consumer graphics card. DeepSeek-Coder-V2 model card
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Qwen3-Coder 30B Q4: an estimate near the 24GB boundary
LocalVRAM estimates 20GB minimum and 22GB optimal VRAM for Qwen3-Coder 30B Q4, and lists 32GB or more system RAM. Since this is a third-party estimate rather than an official hardware requirement or a controlled result, use it as a screening figure. A 24GB GPU may have little spare memory for long contexts, runtime overhead, or other GPU activity. LocalVRAM coding-model estimates
A separate guide describes Qwen3-Coder-30B-A3B-Instruct as 30.5B total / 3.3B active parameters, with a 262,144-token context. That guide’s context figure is a model specification, not evidence that the full context and model will fit on a given GPU. Local AI Models guide
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose by workload, not maximum context
A longer context window can be useful for keeping more code and project material in view, but maximum context is not the same as practical speed, memory fit, or coding quality. Select settings for the task you actually run:
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- Code completion and small edits: begin with a smaller model and a moderate context. This leaves more room for the runtime and other applications.
- Multi-file work: test the context you expect to use with the exact quantization and runtime. More context increases working-memory demand.
- Agentic coding: evaluate the full workflow, including repeated prompts and tool use. A model that loads successfully is not automatically usable at the speed or context your workflow requires.
The available figures support model specifications and one set of third-party fit estimates, not a universal ranking or a hands-on benchmark across runtimes. They also do not establish GPU prices or context-specific memory use on individual systems. Before committing to a model, check the exact quantization file and context setting in your intended inference stack.
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How to test a model on your computer
- Identify dedicated VRAM and system RAM. Do not count system RAM as GPU memory; note whether your runtime will use CPU offload.
- Select the exact model variant and quantization. For MoE models, use total parameters when considering stored weights, not just active parameters.
- Start at a conservative context setting. Do not assume the model’s published maximum context will fit with its weights.
- Load the model and monitor memory during a representative coding task. A short prompt may use less KV-cache memory than the longer project context you intend to use.
- Increase context or enable offload incrementally. If the runtime runs out of memory or becomes impractically slow, reduce context, choose a smaller quantization or model, or use a configuration with more memory.
Why a 16GB VRAM question has no single model answer
“Which local LLMs for coding can run on a computer with 16GB of VRAM?” is a useful way to frame the choice, but it cannot be answered by parameter count alone. One community discussion uses that wording, but it is an example of a reader question rather than evidence of how common the query is. r/LocalLLM discussion
For a 16GB card, use Qwen2.5-Coder 7B as a cautious starting comparison, not a guaranteed fit. Whether a 14B quantized build works depends on its memory footprint, context, runtime, and any offload. For 30B Q4, the cited estimate starts above 16GB, so it is not a sensible default for an all-GPU configuration at that capacity.
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