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The right local AI coding model depends on more than its parameter count: quantization, context length, runtime overhead, and whether you let the system use CPU and RAM all affect what fits. For a practical starting point, compare smaller Qwen2.5-Coder variants on tighter GPUs, consider Qwen3-Coder 30B Q4 only when you have roughly 20–22 GB of VRAM according to a third-party estimate, and treat DeepSeek-Coder-V2’s active-parameter count separately from the memory needed to store its full weights.
How to judge whether a local coding model fits
A model’s parameter count is not a VRAM requirement. The weights are only one part of the working memory: the inference runtime, the selected context length, and the key-value (KV) cache also consume memory. Other GPU workloads reduce what is available to the model.
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- Weights: Total parameters and quantization affect how much memory the model’s stored weights need. The exact amount also depends on the model file and inference runtime.
- Context and KV cache: A model card’s maximum context length is not a promise that the model will run at that length on a particular GPU. Longer context can increase memory use.
- Runtime and other workloads: Inference software has overhead, and applications sharing the GPU leave less memory available.
- System RAM and CPU offload: Some setups can place part of a model in system RAM instead of VRAM. This may make a constrained configuration possible, but it is a different operating setup and can affect performance. LocalVRAM says its planning can include possible CPU spill, especially with long context; its numbers should therefore be read as estimates, not universal GPU requirements.
Mixture-of-experts (MoE) models need an additional distinction: active parameters describe the portion used for each token, while total parameters describe the full model. Active parameters do not tell you how much memory is needed to store the full weights.
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Which models are worth comparing?
| Model | Documented facts | Memory implications |
|---|---|---|
| Qwen2.5-Coder | Qwen lists 0.5B, 1.5B, 3B, 7B, 14B, and 32B variants, with support described up to 128K tokens. Qwen2.5-Coder model card | The 7B and 14B variants are useful comparison points for tighter memory budgets; 32B needs substantially more weight memory. No universal VRAM minimum is established by the cited card. Fit varies with quantization and context. |
| DeepSeek-Coder-V2-Lite | 16B total parameters, 2.4B active parameters, and 128K context. The official card provides BF16 inference code but does not give a universal consumer-GPU minimum in the cited material. DeepSeek-Coder-V2 model card | Its low active count does not remove the memory needed for the full model weights. Check the total model, quantization, context, and runtime before choosing a deployment. |
| DeepSeek-Coder-V2 full | 236B total parameters, 21B active parameters, and 128K context. DeepSeek AI says BF16 full-model inference requires 8 GPUs with 80 GB each. DeepSeek-Coder-V2 model card | The stated BF16 setup is a large multi-GPU deployment, not a typical single consumer GPU. It does not describe requirements for community quantized builds or other inference configurations. |
| Qwen3-Coder 30B Q4 | LocalVRAM estimates 20 GB minimum and 22 GB optimal VRAM, with 32 GB or more of system RAM. LocalVRAM coding-model table | This is a third-party estimate, not an official requirement or a controlled test. A 24 GB GPU may leave limited room for long context or other GPU work. |
| Qwen3-Coder-30B-A3B-Instruct | Local AI Models lists 30.5B total parameters, 3.3B active parameters, and a 262,144-token context. Local AI Models guide | The active count does not represent the full weight-storage requirement. The listed maximum context does not establish that the model fits at that context on any particular machine. |
What fits in 8 GB, 16 GB, and 24 GB of VRAM?
These are planning tiers, not guarantees. The cited material does not establish a tested, context-specific fit for each GPU capacity. Use the table to narrow your candidates, then verify the exact quantized model file and context setting in your own inference software.
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| Dedicated VRAM | Reasonable starting point | What to keep in mind |
|---|---|---|
| 8 GB | Start by comparing smaller Qwen2.5-Coder variants, such as 0.5B through 7B, at the quantization your runtime supports. | No universal minimum or tested fit for a particular variant on 8 GB is established in the available material. Context, runtime overhead, and other GPU use can change whether a setup works. |
| 16 GB | Compare Qwen2.5-Coder 7B and 14B, along with quantized builds of other models whose documented total size and files suit your setup. | The cited forum question, “Which local LLMs for coding can run on a computer with 16GB of VRAM?”, is an example of community phrasing, not evidence that any specific model will fit. r/LocalLLM discussion |
| 24 GB | Qwen3-Coder 30B Q4 is a candidate to investigate: LocalVRAM estimates 20 GB minimum and 22 GB optimal VRAM. | The estimate leaves limited margin at 24 GB for longer context or other GPU workloads, and is not a tested result. Check the exact model file, runtime, context setting, and available memory. |
How to choose for your coding workload
- Code completion and small edits: Begin with a smaller model that fits comfortably, rather than using the largest model your GPU can just accommodate.
- Multi-file or agentic coding: Consider whether the model can run with the context your workflow needs. A high published context limit alone does not establish practical fit or coding quality.
- MoE models: Compare total parameters and the specific quantized build; do not use active parameters as a proxy for stored-weight memory.
- Constrained VRAM: CPU offload may let a setup use system RAM, but evaluate it as a different configuration and account for possible performance effects.
- Buying hardware: Decide whether a smaller model on the GPU you already own is sufficient before spending for more VRAM. The cited material does not provide current GPU prices or a price-to-performance comparison.
What DeepSeek’s BF16 requirement does—and does not—mean
DeepSeek AI’s model card states: “If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.” This is a specific statement about BF16 inference of the full model. It is not a general hardware requirement for every quantized build, precision, or inference runtime.
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