DeepSeek V4 Flash 162B — Hardware Requirements & GPU Compatibility
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DeepSeek V4 Flash 162B is a 92.2B-parameter open language model from 0xSero in the DeepSeek V4 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 55.62 GB of VRAM — see which GPUs and Macs can run it below.
DeepSeek V4 Flash 162B runs on a 64 GB+ Mac or a workstation GPU
Best local pick → Mac Pro M2 Ultra (64 GB) · Q4_K_M · 55.8 GB · ~10 tok/s
Macs from 48 GB (Q2_K · ~11 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run DeepSeek V4 Flash 162B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/0xSero/DeepSeek-V4-Flash-162B-GGUFQ4_K_M needs offload on the RTX 4090 · 31.8 GB short · 16K context
Download: DeepSeek-V4-Flash-162B-Q4_K_M.gguf55.30 GB0xSero
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (40 GB headroom) · Q4_K_M
- Generation speed
- ~21 tok/s
- generation speed
- Cost per 1M output tokens
- $16.33
- per 1M output tokens
How Much VRAM Does DeepSeek V4 Flash 162B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 39.5 GB | 39.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 39.7 GB | |
| 45.3 GB | 45.6 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 45.4 GB | |
| 55.6 GB | 56 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 55.8 GB | |
| 66.0 GB | 66.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 66.2 GB | |
| 76.4 GB | 76.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 76.5 GB | |
| 92.5 GB | 92.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 92.7 GB | |
| 184.7 GB | 185 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 184.8 GB |
Selected Q4_K_M
4-bit medium quantization55.8GBat 16K context
- Weights
- 55.3 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 31.8 GB more
Which Hardware Can Run DeepSeek V4 Flash 162B?
Q4_K_M · 55.6 GBDeepSeek V4 Flash 162B (Q4_K_M) requires 55.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 73+ GB is recommended. Using the full 1049K context window can add up to 11.5 GB, bringing total usage to 67.1 GB. No consumer GPU has enough memory. 52 devices with unified memory can run DeepSeek V4 Flash 162B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
64–80 GB
fits · 8.2 GB free · up to 512K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~10Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~8Simulate output on Mac Studio M5 Max (64 GB)Orin~2Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to DeepSeek V4 Flash 162B
DeepSeek V4 family · 12 models →Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 3.2 90B Vision InstructMeta | — | 199.9 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- 0xSero
- Family
- DeepSeek V4
- Parameters
- 92.2B
- Architecture
- DeepseekV4ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2026-05-27
- License
- MIT
Frequently Asked Questions
How much VRAM does DeepSeek V4 Flash 162B need?
DeepSeek V4 Flash 162B requires 55.6 GB of VRAM at Q4_K_M, or 184.7 GB at BF16. Full 1049K context adds up to 11.5 GB (67.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 92.2B × 4.8 bits ÷ 8 = 55.3 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 11.8 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run DeepSeek V4 Flash 162B?
No — DeepSeek V4 Flash 162B requires at least 39.5 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for DeepSeek V4 Flash 162B?
For DeepSeek V4 Flash 162B, Q4_K_M (55.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (66.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 39.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek V4 Flash 162B on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run DeepSeek V4 Flash 162B, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 55.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).
Can I run DeepSeek V4 Flash 162B locally?
Yes — DeepSeek V4 Flash 162B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is DeepSeek V4 Flash 162B?
At Q4_K_M, DeepSeek V4 Flash 162B can reach ~86 tok/s on AMD Instinct MI350X. 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 ÷ 55.6 × 0.65 = ~94 tok/s
Estimated speed at Q4_K_M (55.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of DeepSeek V4 Flash 162B?
At Q4_K_M, the download is about 55.30 GB. The full-precision BF16 version is 184.33 GB. The smallest option (Q2_K) is 39.17 GB.
Which GPUs can run DeepSeek V4 Flash 162B?
No single consumer GPU has enough VRAM to run DeepSeek V4 Flash 162B at Q4_K_M (55.6 GB). Multi-GPU or professional hardware is required.
Which devices can run DeepSeek V4 Flash 162B?
52 devices with unified memory can run DeepSeek V4 Flash 162B at Q4_K_M (55.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.