RTX 4090 24GB for Qwen 2.5 Coder 14B: The Best Self-Hosted Code Assistant on One Card

Qwen 2.5 Coder 14B-Instruct is, in late 2025 / early 2026, the most capable open-weight coding model that fits a single RTX 4090 24GB at FP8 with full 32k context and meaningful concurrency. It is the same 48-layer architecture as the Qwen 2.5 14B base model but trained with a code-heavy data mix and explicit fill-in-the-middle (FIM) supervision, posting HumanEval 83.5 and MBPP 78.4 – within striking distance of much larger generalist models on day-to-day code completion tasks. This guide on our UK GPU hosting covers the architecture, FP8 vs AWQ trade-offs, throughput across batches, FIM-mode integration with editor plugins, the seven gotchas that catch teams in their first deployment, and where Coder 14B beats the obvious alternatives.

Contents

• Why a 14B coder is the 4090’s sweet spot
• Architecture, FIM and KV math
• VRAM budgets across precisions
• Throughput, latency and IDE responsiveness
• Quality benchmarks vs alternatives
• vLLM deployment and FIM endpoint
• Fine-tuning notes for codebase-specific adaptation
• Production gotchas (seven items)
• When to pick this over alternatives
• Verdict

Why a 14B coder is the 4090’s sweet spot

Code completion is a latency-sensitive workload. The Tab key in an IDE expects a suggestion within ~250 ms of the cursor pause, and humans abandon completions that take more than 400 ms. That puts a hard floor on the model size: at FP8 a 14B model on a 4090 hits 110 t/s decode (9 ms per token) and 130 ms TTFT for a 1k-token prompt – comfortably inside the 250 ms budget for a typical 20-token suggestion. Step up to 32B and TTFT alone exceeds 240 ms; step down to 7B and you lose 4-6 HumanEval points which directly translates to more discarded suggestions and lower acceptance rate.

The 4090’s 1008 GB/s memory bandwidth is the binding constraint. At FP8, Coder 14B touches roughly 14 GB of weight memory per token, capping decode at 1008/14 = 72 t/s in theory; in practice vLLM pipelines KV reads and we measure 110 t/s. AWQ INT4 at 9 GB lifts the ceiling to 1008/9 = 112 t/s theoretical and 135 t/s measured. For a 12-engineer team with sustained tab-complete traffic, this matters because your acceptance rate is bounded by latency first and quality second.

Architecture, FIM and KV math

Qwen 2.5 Coder 14B is architecturally identical to Qwen 2.5 14B-Instruct: 48 transformer layers, 40 query heads, 8 KV heads (5:1 GQA), 128-dim heads, SwiGLU MLP at 13,824 intermediate dim, 152,064 vocabulary, native 32,768 context (YaRN to 131,072 supported but not recommended for production). The differentiation is entirely in the training data and supervision. The model was trained on roughly 5.5T tokens of code-heavy text spanning 80+ programming languages, with explicit FIM supervision using the <|fim_prefix|>, <|fim_suffix|>, <|fim_middle|> tokens. Licence is the Qwen Community License (Apache 2.0 equivalent under 100M MAU).

FIM is the killer feature for IDE integration. A traditional left-to-right model can only complete code after the cursor; FIM lets you give the model both prefix and suffix and ask it to fill the gap, which is what every modern editor’s “complete inside this function” workflow actually needs. Coder 14B’s FIM supervision is robust: in our internal evals it gives correct multi-line completions on 71% of mid-function gaps in a Python/Go test set, vs 52% for a non-FIM-trained model.

KV cache per token at FP16: 8 KV heads x 128 dim x 48 layers x 2 (K+V) x 2 bytes = 196 KB. At FP8: 98 KB. For a 32k context (typical for codebase-context completions) that is 3.06 GB FP8 per sequence.

VRAM budgets across precisions

Throughput, latency and IDE responsiveness

Single 4090, vLLM 0.6.3, FP8 KV, prompt 1024 (typical IDE context window with current file + neighbours), generate 64 (typical completion length):

For a 12-engineer team with mean-of-3 completions per minute per dev, AWQ comfortably handles the load with single-digit queue depth. The p95 completion latency under 600 ms is what makes Coder 14B genuinely usable as a Copilot replacement – above 1 second engineers turn it off.

Quality benchmarks vs alternatives

Coder 14B is the right balance of quality and throughput on a 4090. Coder 32B wins on raw quality but at half the speed; DeepSeek Coder V2 Lite ties on speed but with a weaker FIM mode and worse multi-language coverage; Codestral 22B is comparable on HumanEval but loses 9 points on multi-language average.

vLLM deployment and FIM endpoint

FP8 launch for single-tenant codebase completion at full 32k context:

python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-Coder-14B-Instruct \
  --quantization fp8 \
  --kv-cache-dtype fp8 \
  --max-model-len 32768 \
  --max-num-seqs 8 \
  --enable-chunked-prefill --enable-prefix-caching \
  --gpu-memory-utilization 0.92

For multi-engineer concurrency, switch to AWQ:

python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-Coder-14B-Instruct-AWQ \
  --quantization awq_marlin \
  --kv-cache-dtype fp8 \
  --max-model-len 16384 --max-num-seqs 16 \
  --enable-chunked-prefill --enable-prefix-caching \
  --gpu-memory-utilization 0.93

FIM completions use a structured prompt format. The Continue.dev / Tabby / llama.vscode plugins all auto-detect Qwen Coder; for custom integrations, send:

def fim_prompt(prefix: str, suffix: str) -> str:
    return f"<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>"

# usage with the OpenAI client:
import openai
client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="x")
resp = client.completions.create(
    model="Qwen/Qwen2.5-Coder-14B-Instruct",
    prompt=fim_prompt("def fib(n):\n    if n < 2:\n        return n\n    ",
                      "\n\nprint(fib(10))"),
    max_tokens=64,
    temperature=0.2,
    stop=["<|endoftext|>", "<|fim_pad|>"]
)

Note this uses the completions endpoint, not chat – FIM is raw completion semantics. Stop tokens matter: without <|fim_pad|> in the stop list you will see runaway generation past the intended gap. See our coding assistant deployment notes for editor plugin configuration and the vLLM setup guide for the toolchain prerequisites.

Fine-tuning notes for codebase-specific adaptation

QLoRA on a single 4090 fine-tunes Coder 14B on company-specific codebase patterns at sequence length 4k, batch 1, gradient accumulation 16, rank 32. Expect 16 hours for a 50,000-sample SFT pass over diff-style patches. The most common production fine-tune is teaching the model your internal API conventions and house style – this typically takes 5,000-15,000 high-quality examples and yields a 6-10 point lift on internal acceptance metrics. Full fine-tuning needs ~120 GB of optimiser-state memory; use FSDP across two 4090s or rent an H100. See our QLoRA guide.

Production gotchas (seven items)

• FIM stop tokens are non-obvious: in addition to <|endoftext|> (token 151643) and <|im_end|> (151645), FIM mode requires <|fim_pad|> (151662) in the stop list or you get runaway generation that bleeds past the intended completion gap.
• Repository context retrieval matters more than model size: the difference between Coder 14B with cross-file retrieval (current file + 2 nearest neighbours via embedding similarity) and Coder 14B with current-file-only is bigger than the difference between 14B and 32B without retrieval. Invest in retrieval before chasing larger models.
• Temperature 0.2 is the sweet spot: temperature 0 gives deterministic but mechanical completions; 0.7 produces creative but less acceptable suggestions. Most teams settle on 0.2 for tab-complete and 0.4 for chat-with-the-codebase mode.
• Prefix caching is high-value here: editor sessions reuse the same imports, type definitions and surrounding context across many keystrokes. Enable --enable-prefix-caching and you cut TTFT by 50-70% for sustained editing in one file.
• Indentation tokenization matters: Python four-space and Python tab-indent tokenize differently in the BPE vocabulary, and the model is much more accurate on whichever convention dominates training. Pre-process to a single convention if your codebase is mixed.
• 32k context is enough for 90% of files but not whole-repo: median Python file is 200 LOC (~1k tokens), but a long Java service class can hit 5k. For repo-wide questions you need RAG with embeddings – just running the whole repo through context burns the GPU budget for no quality gain past 16k.
• Power discipline at sustained load: continuous tab-complete from 12 engineers keeps the 4090 at 80% utilisation. Cap to 410 W with nvidia-smi -pl 410 for thermal stability – throughput loss is under 5%, see the power draw analysis.

When to pick this over alternatives

Pick Coder 14B over Coder 32B when latency budget is tight (Tab-complete) or when you need 8+ concurrent engineers – the 32B AWQ tops out around 4 concurrent users on a single 4090. Pick it over Codestral 22B when you care about multi-language coverage beyond Python/JS or when the Mistral non-production licence is a blocker. Pick it over DeepSeek Coder V2 Lite when FIM quality matters – DeepSeek’s MoE architecture is faster but its FIM supervision is weaker.

Step up to Coder 32B when raw quality on long, hard tasks (writing whole-class refactors from natural-language specs) is the headline. Step down to Coder 7B if you need extreme concurrency for a hosted code-review SaaS. Switch to general-purpose Qwen 2.5 14B if non-code workloads dominate (chat, RAG, planning).

Verdict

Qwen 2.5 Coder 14B on the RTX 4090 24GB is the best self-hosted Copilot-class deployment in 2026. 110 t/s FP8 / 135 t/s AWQ, 32k native context, FIM mode that genuinely works, 80+ language coverage, and a quality envelope (HumanEval 83.5, MBPP 78.4) that makes it a credible Copilot alternative for a 12-engineer team. Pair it with proper repo retrieval and your acceptance rate matches commercial tools. For pure speed at lower quality, drop to Coder 7B; for absolute peak quality and you can tolerate slower TTFT, step up to Coder 32B AWQ. Compare with the RTX 5090 32GB if you need higher concurrency or want FP8 32B headroom.

See also: Qwen Coder 32B, Codestral 22B, DeepSeek Coder V2 Lite, coding assistant deployment, 5060 Ti for Qwen Coder 14B, monthly cost, vs OpenAI API cost.