the best local model with 19gb of vram for codeing

Claude Opus 4.7

Answer: For coding with ~19GB of VRAM, the best local model is Qwen2.5-Coder-32B-Instruct at 4-bit quantization (Q4_K_M/AWQ), with Qwen2.5-Coder-14B at Q6_K/Q8_0 as the recommended fallback when long context is critical.

I'm maintaining my position, which aligns with Kimi K2.6 and Gemini 3.1 Pro Preview. The core argument hasn't been undermined: Qwen2.5-Coder-32B at Q4_K_M occupies roughly 17–18GB and remains the top-scoring open-weight coding model on HumanEval, MBPP, LiveCodeBench, and Aider, beating Codestral 22B and DeepSeek-Coder-V2-Lite on coding accuracy. Claude Opus 4.8 makes a reasonable case for Qwen2.5-Coder-14B at Q8_0/Q6_K, and it's a legitimate trade-off — but the claim that 14B "frequently matches or outperforms" the 32B variant on coding benchmarks is not supported by Qwen's own published evaluations, where the 32B clearly leads. The 14B route is better framed as a context-length optimization (which I already flagged in my original answer), not as the outright best model. DeepSeek V4 Pro's suggestion of offloading a Mixtral 8x22B to system RAM contradicts the spirit of the question ("with 19GB of VRAM") — partial CPU offload yields single-digit tokens/sec and is unpleasant for interactive coding. And DeepSeek-Coder-V2-Lite, while excellent and fast, still trails Qwen2.5-Coder-32B on hard coding tasks. The nuanced answer remains: run Qwen2.5-Coder-32B Q4_K_M as the default for maximum code quality, and step down to Qwen2.5-Coder-14B at Q6_K only when you regularly need >16k token contexts. That's the best use of a 19GB budget today.

Gemini 3.1 Pro Preview (changed mind)

Answer: The best local model for a 19GB VRAM limit is Qwen2.5-Coder-14B-Instruct at Q6_K or Q8_0 quantization, as it provides top-tier coding capabilities while leaving ample VRAM for large context windows.

Claude Opus 4.8 makes a highly practical and convincing argument regarding the importance of context windows in modern coding workflows. While I initially advocated for cramming a 32B model into 19GB using 4-bit quantization, Claude Opus 4.8 correctly highlights that doing so leaves merely 1-2GB for the KV cache. When using coding assistants like Aider or Continue.dev, or when pasting multiple files to debug complex architectural issues, a small context window will lead to constant Out-Of-Memory (OOM) errors and severe workflow interruptions. Stepping down to Qwen2.5-Coder-14B-Instruct is the perfect compromise. At Q8_0 or Q6_K quantization, the model retains near-fp16 reasoning performance while consuming only 12 to 15 GB of VRAM. This leaves 4 to 7 GB entirely dedicated to context, unlocking massive 32k+ token windows. Ultimately, a highly capable 14B model that can process your entire codebase simultaneously is vastly superior to a 32B model that can only read a few hundred lines before running out of memory. This approach maximizes hardware utilization without sacrificing speed or stability.

Claude Opus 4.8 (changed mind)

Answer: The best local model for coding within a 19 GB VRAM limit is Qwen2.5-Coder-32B-Instruct at 4-bit quantization, specifically Q4_K_M.

After reviewing the consensus from the other panelists, I have revised my initial recommendation. My original stance favored Qwen2.5-Coder-14B at high quantization to preserve precision and context length. However, the compelling arguments from multiple models—particularly the detailed breakdown by Claude Opus 4.7 and Gemini 3.1 Pro Preview—have convinced me that the raw coding intelligence of the 32B model, even when compressed, significantly outweighs the benefits of a perfectly lossless 14B model. Qwen2.5-Coder-32B-Instruct is, by benchmark standards (HumanEval, LiveCodeBench, Aider), a generational leap above the 14B version, competing directly with frontier closed-source models like GPT-4o. At Q4_K_M quantization, the model weights occupy approximately 17-18 GB of VRAM. This fits the 19 GB budget with a small but functional remaining margin for the KV cache, allowing for standard context windows of 4k-8k tokens. I was initially concerned about the memory overhead, but the panel correctly argued that the degradation in reasoning from 14B to 32B is far more damaging to coding accuracy than the precision drop from Q8 to Q4 for the larger model. I was specifically swayed by the point that if fitting the model into VRAM is tight, one can cap the context length slightly rather than downgrading the model's parameter count. The 14B model at Q8_0 remains an excellent fallback for users who routinely need a massive 32k+ context window for monolithic codebases, but for the highest quality code generation and complex debugging, maximizing parameter count with Qwen2.5-Coder-32B at 4-bit is the objectively superior strategy for a 19 GB budget.

DeepSeek V4 Pro

Answer: While Qwen2.5-Coder-32B at 4-bit is a strong contender for raw benchmark scores, my original recommendation of a fully-on-GPU DeepSeek Coder V2 (16B) remains the better practical 'best' for a strict 19GB limit when you factor in real-world context window needs and inference speed.

The other models make a compelling case for the Qwen2.5-Coder-32B's superior benchmark performance. It is indeed state-of-the-art. However, their arguments inadvertently highlight the critical flaw in that choice for a capped 19GB environment, which is why I maintain my position. Running a Qwen2.5-Coder-32B at Q4_K_M requires 16-18GB for the weights alone, as multiple responses confirm. This leaves a razor-thin 1-3GB for the KV cache. This is not merely 'limited,' as some suggest; it is crippling for modern coding assistance. Even Kimi K2.6 admits this setup only allows for a 'short-to-moderate context lengths (e.g., 2k–4k tokens).' A 4k context window is completely inadequate for real-world tasks like debugging a multi-file project, refactoring a class, or having a continued interactive session with an AI coding assistant like Aider or Continue.dev. You would be fighting constant out-of-memory errors the moment the conversation history or code snippet grows beyond trivial size. In contrast, a fully GPU-resident DeepSeek Coder V2 (16B) at Q6_K or Q8_0 consumes roughly 12-14GB. This leaves a comfortable 5-7GB buffer for the KV cache, allowing for massive 32k+ token context windows. For actual coding workflows, having a powerful 16B model with a giant working memory is far more productive than having a smarter 32B model that can barely remember the function you just wrote. The alternative suggestion of a highly-quantized Mixtral 8x22B suffers from the same context limitation and the severe speed penalty of offloading. Therefore, for a strict 19GB limit where both model quality and practical, sustained usability with large context must be optimized, the DeepSeek Coder V2 (16B) is the superior recommendation.

Kimi K2.6

Answer: Qwen2.5-Coder-32B-Instruct at Q4_K_M quantization remains the best local coding model for a 19 GB VRAM budget.

The roundtable consensus from Claude Opus 4.7 and Gemini 3.1 Pro Preview reinforces my original position, while the dissenting arguments do not withstand scrutiny. Claude Opus 4.8 incorrectly claims that a 32B model requires "severe quantization (like Q2 or Q3)" to fit inside 19 GB; in reality, the Q4_K_M weights for Qwen2.5-Coder-32B occupy roughly 18–18.5 GB, leaving sufficient headroom for a modest KV cache at short-to-moderate context lengths (e.g., 2k–4k tokens). The quality loss from Q4_K_M is minimal, and the empirical coding benchmarks (HumanEval, MBPP, LiveCodeBench, Aider) consistently show that 32B parameters at Q4_K_M outperform a 14B model at Q8_0 or Q6_K, because raw model capacity outweighs per-weight bit depth for complex reasoning and code generation. DeepSeek V4 Pro’s recommendation of DeepSeek-Coder-V2 16B is a strong alternative for speed, but it trails Qwen2.5-Coder-32B on most coding leaderboards. If your workflow demands extremely long contexts beyond 8k tokens, then falling back to Qwen2.5-Coder-14B at Q6_K or Q8_0—as Gemini 3.1 Pro Preview and Claude Opus 4.7 both note—is a pragmatic compromise. For pure coding performance under a strict 19 GB limit, however, Qwen2.5-Coder-32B Q4_K_M is still the optimal choice.

Final Summary

The AI Roundtable recommended Qwen2.5-Coder-32B-Instruct (Q4_K_M) as the top choice, with Claude Opus 4.7, Kimi K2.6, and a converted Claude Opus 4.8 arguing its elite reasoning outweighs tight VRAM limits. In a comical twist, Gemini 3.1 Pro Preview and Claude Opus 4.8 completely swapped positions during debate, with Gemini ultimately choosing Qwen2.5-Coder-14B for better context headroom. Meanwhile, DeepSeek V4 Pro stood firm on DeepSeek Coder V2 (16B) as the best practical balance between speed and memory.

3 of 5 models agreed