A user shares their experience running the DeepSeek v4 Flash model with a 24GB GPU and DDR5 RAM, including performance numbers and tips for optimization.
Disclosure: No AI was used to write this My specs are: RTX 4090 128 GB DDR5 5600 MT/s Intel Core Ultra 7 270k Running nvidia-595 on ubuntu 26.04 with latest llama.cpp build (pulled and rebuilt this morning). Tried a lot of things, ended up running unsloth's UD-Q2_K_XL quant with command: taskset -c 0-7 /home/kevin/ai/llama.cpp/build/bin/llama-server -lv 4 -m /home/kevin/ai/models/DeepSeek-V4-Flash-UD-Q2_K_XL-00001-of-00003.gguf --temp 1.0 --top-p 1.0 --min-p 0.0 -t 8 -fitc 64000 -fa off -np 1 Speed: [ Prompt: 132.5 t/s | Generation: 10.9 t/s ] Some notes: On Intel Core Ultra 7 270k (I recently bought this CPU), pinning pcores makes a big difference. Like 2x, from 6.8 tok/s to 11 tok/s --no-mmap is much slower using -ctk q8_0 or -ctv q8_0 crashed the llama.cpp process adjusting -b or -ub to > 4096 with context > 32k seems to explode the CUDA buffer to 90 GB+ with llama-server, for some reason, -fa off is necessary, otherwise it also explodes the CUDA buffer Overall, seems smarter compared to Qwen 3.6 27B Q4_K_XL. It runs slower, but reasons less, meaning tasks still complete in a reasonable amount of time. However, for agentic, Qwen 3.6 27B is still much better because it runs so much faster, and also qwen models don't seem to "over-reason" too much when doing agentic tasks. With a couple fixes (flash attention, microbatch/batch adjust, context quantisation, etc.) I think this model could be pretty decent on 4090/3090. If we could get these numbers up to like ~20 tg/s and ~300 pp/s it might replace qwen 3.6 27b for me. Also tried running IQ4_NL quant but ended up being too slow and couldn't fit enough context (only about 10k): taskset -c 0-7 /home/kevin/ai/llama.cpp/build/bin/llama-cli -m /home/kevin/ai/models/DeepSeek-V4-Flash-UD-IQ4_NL-00001-of-00004.gguf --temp 1.0 --top-p 1.0 --min-p 0.0 -t 8 It was at speed: [ Prompt: 50.7 t/s | Generation: 8.1 t/s ] I am posting this in case its useful to anyone else with a 24GB GPU + consumer RAM. Mostly I see people posting here with M3 Ultras and RTX PRO 6000s lol
User shares optimization benchmarks for DeepSeek-V4-Flash (Q2_K) running on an RTX 5090 using a fork of llama.cpp, achieving 21.3 tokens/s generation and 1 million context size.
A Reddit user shares their experience running DeepSeek V4 Flash on a dual-ASUS GX10 DGX Spark setup, detailing performance metrics, configuration, and power consumption, with throughput benchmarks across various context lengths.
DeepSeek V4 Flash GGUF quantizations have been released by antirez, enabling the model to run on single GPUs like the RTX Pro 6000 and Macs with 128GB+ RAM. The quantized files are available on Hugging Face with instructions for the DS4 inference engine.
Testing DeepSeek v4 Flash on the AMD Ryzen AI Max+ 395 with 128GB RAM achieves ~15 TPS for a 284B MoE model (13B active) locally, costing $3,000 versus $25,000+ for a datacenter setup, highlighting the feasibility of running large models on consumer hardware.
DeepSeek V4 Flash (98GB) now runs up to 7 tokens per second on a single RTX 4060 Ti with CPU offloading, a 3x speed improvement over the previous week's 2 t/s.