How big is the DeepSeek V4 Flash GGUF download?

The 0731 build from unsloth is 155 GB at UD-Q4_K_XL (4 bit) and 162 GB at UD-Q8_K_XL (8 bit). The gap is small because the MoE expert weights dominate the file and quantize efficiently, so Q8 is worth it if your memory allows.

Can a gaming PC with 32 or 64 GB of RAM run DeepSeek V4 Flash?

Not the full model; the weights alone need about 155 GB. The realistic option for that class of machine is the Qwen 3.5 9B distill of V4 Flash, a 5.3 GB download that runs on 8 GB of RAM or a 6 GB GPU.

How fast is DeepSeek V4 Flash on CPU plus GPU?

Because only 13B parameters are active per token, hybrid setups reach interactive speeds. Ballpark on a modern DDR5 workstation with expert offload: mid single digits to mid teens in tokens per second, scaling with memory bandwidth. Apple silicon with high unified memory bandwidth sits at the upper end.

Does the 1M token context work on local hardware?

Technically yes, practically no. KV cache memory grows with context, and very long windows add tens of GB on top of the weights. Locally, 32K to 128K context is the sweet spot; use a hosted option when you need the full million.

Can You Run DeepSeek V4 Flash Locally? The Real Hardware Math

Short answer: yes, on the right machine, and more machines qualify than you might think. Unlike the recent giants (Kimi K3 at 2.8T, V4 Pro at 1.6T), DeepSeek V4 Flash was shaped for exactly this question: 284B total parameters, 13B active, MIT weights, GGUFs on day one. Here is the full math, so you know before you start a 155 GB download. (Setup steps live in the local guide.)

The Two Numbers That Decide Everything

MoE models split the hardware question in two. Total parameters (284B) decide how much memory you need: every expert must sit somewhere, RAM or VRAM, whether or not it fires. Active parameters (13B) decide how fast it runs: each token only touches a 13B slice, so the compute load resembles a 13B model, not a 284B one. That is why V4 Flash on a big RAM workstation is usable while a dense 284B model at the same size would crawl.

Download Sizes

Build	Quant	Size
V4 Flash 0731 (unsloth)	UD-Q4_K_XL	155 GB
V4 Flash 0731 (unsloth)	UD-Q8_K_XL	162 GB
V4 Flash Preview (unsloth, in app catalog)	UD-Q4_K_XL	144 GB
V4 Flash Abliterated (huihui)	Q4_K	154 GB
Qwen 3.5 9B V4 Distill	Q4_K_M	5.3 GB

Note the odd pair at the top: 8 bit is only 7 GB bigger than 4 bit, because the expert weights dominate the file and pack tightly. If you clear the memory bar for Q4, check whether you clear it for Q8 too.

Machine Classes, Rated Straight

Machine	Verdict	Notes
Mac Studio, 192 GB unified	Yes, comfortable	Q4 fits with context headroom; high memory bandwidth helps speed
Workstation, 256 GB DDR5 + 24 GB GPU	Yes	Attention and shared layers on GPU, experts in RAM; interactive speeds
Workstation, 192 GB DDR5, no GPU	Yes, slower	Pure CPU works because of the 13B active slice; be patient on prompt processing
128 GB RAM anything	Not the Q4 build	Wait for smaller dynamic quants, or take the distill
Gaming PC, 32 to 64 GB RAM	Full model: no	Qwen 3.5 9B V4 Distill (5.3 GB) is the play
Any laptop	Distill or cloud	See the cloud guide

Speed Expectations

Rough guidance, not lab numbers: with experts in system RAM and a GPU carrying the shared layers, a modern DDR5 workstation lands somewhere between mid single digits and the mid teens in tokens per second; memory bandwidth is the lever that moves you inside that range. Apple silicon with its unified memory bandwidth sits near the top. Prompt processing on long inputs is the painful part of hybrid setups; if your workflow feeds the model whole repositories, plan around that or go hosted.

The Context Window Reality

V4 Flash advertises 1M tokens of context, and the hybrid compressed attention design is what makes that affordable upstream. Locally, KV cache still costs real memory that scales with the window, stacking tens of GB on top of the weights as you push six figures. Practical local advice: run 32K to 128K, which covers almost every chat and coding session, and rent the full million from a hosted endpoint the day you need to feed it a whole codebase at once.

If the Math Says No

Two good exits. The Qwen 3.5 9B V4 Distill carries the V4 Flash reasoning style in a 5.3 GB file, runs on nearly anything, and sits one click away in the Locally Uncensored model catalog next to the other strong local picks. Or skip the hardware entirely: LU Labs hosts V4 Flash (and V4 Pro) on flat monthly plans, covered in the cloud guide.