Qwen2.5-Coder 7B · 24 GPUs measured first-party · llama.cpp Q4_K_M · Updated October 2026

What GPU Do You Need for Qwen2.5-Coder 7B?

Qwen2.5-Coder 7B is the autocomplete tier: small enough to fit an 8GB card (~6GB measured peak), fast enough that completions feel instant: 287 tok/s on the B300, 266 on the H200, and still 38 tok/s on a seven-year-old T4. We measured it on 24 GPUs with llama.cpp at Q4_K_M, logging power and VRAM on every run.

Benchmarked weights: bartowski/Qwen2.5-Coder-7B-Instruct-GGUF

Fastest we measured

NVIDIA B300

286.5 tok/s on Qwen2.5-Coder 7B, the ceiling. Measured on our bench. 288GB of VRAM, $40,000 at launch.

Pros

• 286.5 tok/s on Qwen2.5-Coder 7B
• 288GB, clears the Qwen2.5-Coder 7B floor
• Rentable by the hour rather than bought

Cons

• 1400W board rating
• Datacenter or workstation hardware, not a retail purchase

Best consumer card

NVIDIA GeForce RTX 5090

284.6 tok/s on Qwen2.5-Coder 7B, fastest card you can buy at retail. Measured on our bench. 32GB of VRAM, $1,999 at launch.

Pros

• 284.6 tok/s on Qwen2.5-Coder 7B
• 32GB, clears the Qwen2.5-Coder 7B floor

Cons

• 575W board rating

Cheapest card that runs it

NVIDIA GeForce GTX 1660 Super

49.04 tok/s on Qwen2.5-Coder 7B, lowest launch price that still fits. Measured on our bench. 6GB of VRAM, $229 at launch.

Pros

• 49.04 tok/s on Qwen2.5-Coder 7B
• 6GB, clears the Qwen2.5-Coder 7B floor

Cons

• 125W board rating

Best value

NVIDIA GeForce RTX 5060

81.99 tok/s on Qwen2.5-Coder 7B, most speed per dollar. Measured on our bench. 8GB of VRAM, $249 at launch. That is 329.3 tok/s per $1,000 of launch price.

Pros

• 81.99 tok/s on Qwen2.5-Coder 7B
• 8GB, clears the Qwen2.5-Coder 7B floor

Cons

• 145W board rating

The right tool for inline completion. Code completion has a latency budget of a few hundred milliseconds. The model has to produce a usable suggestion before you type the next character. That's a speed problem, not an intelligence problem, and a 7B coder model is the honest answer to it. My recommended split for a local stack: this model handles fill-in-middle and autocomplete, while a 30B-class model (ideally the Qwen3 Coder MoE) handles the chat-and-refactor side. On a 24GB card you can run both simultaneously.

Numbers worth noticing. The H200's 266 tok/s at 132W (2.02 tok/W) doubles the efficiency of the chart-topping B300, the recurring Hopper-vs-Blackwell pattern in our small-model data. But the more practical row is further down: 6GB-class cards clear the floor, and the $179 Arc A580 fits it. For a completions model that runs all day next to your IDE, low idle-adjacent power draw matters more than peak throughput. This is a workload where a small efficient card is genuinely the better engineering choice.

About Qwen2.5-Coder 7B. Qwen2.5-Coder 7B: from Qwen, 7.6B parameters, on Hugging Face since September 2024, Apache 2.0 licence. 5,580,328 downloads in the last 30 days and 7 community quantizations.

How it compares. H100 80GB HBM3: Qwen2.5-Coder 7B 263.9 tok/s, Qwen2.5-7B 264.2 (8B), Mistral-7B-Instruct-v0.2 276.7 (7B), Llama-3.1-8B 261.8 (8B), Llama 3 8B 264.4 (8B). 3 of 4 beat Qwen2.5-Coder 7B here.

Cost on a rented GPU. 1M generated tokens of Qwen2.5-Coder 7B: $0.15 on a RTX 3060 ($0.036/hr, 4.1 hours), $6.73 on a B300 ($6.94/hr, 58 min, 45.5x the cost).

Speed tiers for Qwen2.5-Coder 7B. 30+ tok/s: 24 (RTX 5090, RTX 4090, RTX 5080). 30 tok/s is roughly where replies outpace reading.

Reading your prompt. Before Qwen2.5-Coder 7B writes anything it reads the input: 15688.8 tok/s on the RTX 5090 (0.3s for a 4,000-token prompt), 12068.8 on the RTX 4090 (0.3s), 161.1 on the GTX 1660 Super (24.8s). Long documents and big code files feel this number more than the generation speed.

VRAM for Qwen2.5-Coder 7B. Measured peak 4.4GB, so 8GB is the smallest common card size; smallest card it ran on: GTX 1660 Super (6GB). With long context: Q4_K_M 6GB (tested), Q2_K 4GB, Q3_K_M 5GB, Q5_K_M 7GB, Q6_K 9GB.

Power on Qwen2.5-Coder 7B. Most efficient: A100 80GB SXM4, 114W, 0.19 kWh per 1M generated tokens. Hungriest: B200, 341W, 0.34 kWh. At $0.15/kWh: $0.029 per 1M generated tokens.

Our verdict

Qwen2.5-Coder 7B: 287 tok/s peak, ~6GB floor, instant-feel completions on almost any modern card. Use it as the fast half of a local coding stack, autocomplete here, a 30B-class model for the heavy lifting, and an 8GB card is all the hardware this half needs.

FAQ

Can an 8GB card run Qwen2.5-Coder 7B?

Comfortably, ~6GB measured peak at Q4_K_M leaves context headroom on 8GB. The $179 Intel Arc A580 clears it; so does an RTX 3050.

Is a 7B model good enough for coding?

For inline autocomplete and fill-in-middle, yes, that workload rewards speed over depth. For multi-file refactors and architectural questions, pair it with Qwen3 Coder 30B-A3B or another 30B-class model.

How fast is it in practice?

287 tok/s on the B300, 266 on the H200, but even the old T4's 38 tok/s outruns human reading speed. On any modern consumer card, completions are effectively instantaneous.

Qwen2.5-Coder 7B or the newer Qwen3 8B?

For code, the coder tune wins, fill-in-middle training matters for completions. Qwen3 8B is the better general assistant. Same VRAM class (~6GB), so your card runs either.

What does a full local coding stack look like?

Our suggested pairing: this model for completions plus Qwen3 Coder 30B-A3B for chat/agent work, together they fit in 24GB. On an 8GB card, run the 7B alone and let a cloud model handle the big tasks.