Deepseek Coder 7B Instruct V1.5 — Hardware Requirements & GPU Compatibility
ChatCode
Deepseek Coder 7B Instruct V1.5 is a 6.9-billion-parameter code-focused language model from DeepSeek, fine-tuned from the DeepSeek-LLM 7B base for programming assistance, code generation, and general chat. It continues DeepSeek's original Coder line, trained on roughly 2 trillion tokens of code-heavy text before instruction tuning, and its default system prompt frames it specifically as a programming assistant. Its size makes it well suited to local deployment on a single mainstream consumer GPU with around 8GB or more of VRAM once quantized. Context length is limited to 4,096 tokens, short by current standards. It is released under DeepSeek's own custom license (listed as "Other"), so commercial users should review the license terms before deployment. Published in January 2024, it is one of DeepSeek's earlier widely adopted open-weight coding assistants.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~120 tok/s · 4K)Macs from 8 GB (IQ2_S · ~21 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Deepseek Coder 7B Instruct V1.5
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/deepseek-coder-7b-instruct-v1.5-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.5 GB free · 4K context
Download: deepseek-coder-7b-instruct-v1.5.i1-Q4_K_M.gguf4.22 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (5 GB headroom) · Q4_K_M
- Generation speed
- ~43 tok/s
- generation speed
- Cost per 1M output tokens
- $0.54
- per 1M output tokens
How Much VRAM Does Deepseek Coder 7B Instruct V1.5 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~155 tok/s | |
| 4.3 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~151 tok/s | |
| 4.7 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~140 tok/s | |
| 4.8 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~138 tok/s | |
| 5.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~120 tok/s | |
| 6.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~105 tok/s | |
| 7.0 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~94 tok/s | |
| 8.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~80 tok/s |
Selected Q4_K_M
4-bit medium quantization6.5GBat 4K context
- Weights
- 4.1 GB
- KV cache
- 2.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.5 GB free · ~120 tok/s
≥50 tok/s needs ≥419 GB/s · RTX 5090 ~214, RTX 3090 Ti ~120
Which Hardware Can Run Deepseek Coder 7B Instruct V1.5?
Q4_K_M · 5.5 GBDeepseek Coder 7B Instruct V1.5 (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 4K context window can add up to 1.0 GB, bringing total usage to 6.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Deepseek Coder 7B Instruct V1.5, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.5 GB free · up to 4K ctx
16 GPUs · 1 device family
RTX 3080~91Simulate output on RTX 3080RTX 2080 Ti~74Simulate output on RTX 2080 TiRTX 3070 Ti~73Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.5 GB free · up to 4K ctx
1 GPU
RX 7900 XT~88Simulate output on RX 7900 XT32–40 GB
fits · 25.5 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~214Simulate output on RTX 5090M5 Max36 GB~59Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Deepseek Coder 7B Instruct V1.5
DeepSeek Coder family · 11 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 6.9B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-01-25
- License
- Other
Frequently Asked Questions
How much VRAM does Deepseek Coder 7B Instruct V1.5 need?
Deepseek Coder 7B Instruct V1.5 requires 5.5 GB of VRAM at Q4_K_M, or 15.1 GB at BF16. Full 4K context adds up to 1.0 GB (6.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.9B × 4.8 bits ÷ 8 = 4.1 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.4 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Deepseek Coder 7B Instruct V1.5?
For Deepseek Coder 7B Instruct V1.5, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Deepseek Coder 7B Instruct V1.5 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Deepseek Coder 7B Instruct V1.5, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Deepseek Coder 7B Instruct V1.5 locally?
Yes — Deepseek Coder 7B Instruct V1.5 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Deepseek Coder 7B Instruct V1.5?
At Q4_K_M, Deepseek Coder 7B Instruct V1.5 can reach ~881 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~120 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.45 × 0.65 = ~954 tok/s
Estimated speed at Q4_K_M (5.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Deepseek Coder 7B Instruct V1.5?
At Q4_K_M, the download is about 4.15 GB. The full-precision BF16 version is 13.82 GB. The smallest option (IQ2_XXS) is 1.90 GB.
Which GPUs can run Deepseek Coder 7B Instruct V1.5?
52 consumer GPUs can run Deepseek Coder 7B Instruct V1.5 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Deepseek Coder 7B Instruct V1.5?
151 devices with unified memory can run Deepseek Coder 7B Instruct V1.5 at Q4_K_M (5.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.