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Qwen3.8-Flash-Next-GGUF

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Correct me if I'm wrong but threads 12 does not make any difference, right? Because you are offloading nothing to the cpu (cpu-moe 0). In my experience if you are able to raise batch size and ubatch size you will get better tokenization performance. In a longer context I'm getting around 200-220t/s with a 5070Ti and a 3090.

Correct me if I'm wrong but threads 12 does not make any difference, right? Because you are offloading nothing to the cpu (cpu-moe 0). In my experience if you are able to raise batch size and ubatch size you will get better tokenization performance. In a longer context I'm getting around 200-220t/s with a 5070Ti and a 3090.

220 t/s, how? What's your config?

Correct me if I'm wrong but threads 12 does not make any difference, right? Because you are offloading nothing to the cpu (cpu-moe 0). In my experience if you are able to raise batch size and ubatch size you will get better tokenization performance. In a longer context I'm getting around 200-220t/s with a 5070Ti and a 3090.

220 t/s, how? What's your config?

Sorry for the late answer, I was out sick.

[Qwen3.8-Flash-Next:Q4]
jinja = true
threads = 26
threads-batch = 32
model = /mnt/nvme/llm/hf/Qwen3.8-Flash-Next-AD-4.27bpw-Q4_K_M-M64/Qwen3.8-Flash-Next-AD-4.27bpw-Q4_K_M-M64-00001-of-00033.gguf
split-mode = layer
c = 131072
n-cpu-moe = 24
fa = on
tensor-split = 37/11
fit = off
n-gpu-layers = 999
temperature = 1
top-p = 0.95
top-k = 20
min-p=0.0
cache-type-k = q4_0
cache-type-v = q4_0
load-mode = mmap
batch-size = 2048
ubatch-size = 2048
no-mmproj = true
parallel = 1
chat-template-kwargs = {"reasoning_effort":"medium"}
reasoning-preserve = true
fit-target = 2048
main-gpu = 1
lazy-mode = off
reasoning-format = deepseek
no-op-offload = true

Roughly 12 hours ago I updated llamacpp and since then I'm getting 250+t/s input token generation speed (after warmup). Both of my cards are power limited to 250W.

Roughly 12 hours ago I updated llamacpp and since then I'm getting 250+t/s input token generation speed (after warmup). Both of my cards are power limited to 250W.

That's too slow I guess, I got around 700-800+ PP and 70-90+ TG with Dual 3090.

root@yasei-1:/data# ./llama.cpp.sh
0.00.000.503 I srv  llama_server: initializing ...
0.00.577.647 W DEPRECATED: argument '--model-draft' specified multiple times, use comma-separated values instead (only last value will be used)
0.00.584.799 I cmn  common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)
0.00.585.286 W srv  llama_server: security: no API key is set and CORS allows all origins (see https://github.com/ggml-org/llama.cpp/pull/25655)
0.00.585.293 W srv  llama_server: security: MCP proxy (experimental) enabled - do not expose to untrusted environments
0.00.586.536 I srv    load_model: loading model '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64-00001-of-00033.gguf'
0.02.201.176 E llama_init_from_model: failed to initialize the context: qwen4exp requires ctx_other to be set (this warning is normal during memory fitting)
0.02.255.224 W operator(): failed to measure the memory of the extra model, fitting without it: failed to create llama_context from model
0.16.682.288 I cmn          init: llama threadpool init, n_threads = 12
0.16.870.815 I common_speculative_init_result: loading draft model '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/mtp-Qwen3.8-Flash-Next-shared-Q8_0.gguf'
0.18.928.645 I srv    load_model: loaded multimodal model, '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/mmproj-Qwen3.8-Flash-Next-BF16.gguf'
0.18.928.674 I srv    load_model: initializing, n_slots = 1, n_ctx_slot = 262144, kv_unified = 'true'
0.19.883.362 I srv  llama_server: model loaded
0.19.883.374 I srv  llama_server: listening on http://0.0.0.0:8080
0.19.883.374 W srv  llama_server: notice: server default port will be changed to :9931 in a future release (ref: https://github.com/ggml-org/llama.cpp/pull/26508)
0.23.968.000 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
0.23.968.318 I slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
0.28.780.746 I slot print_timing: id  0 | task 0 | prompt processing, n_tokens =   3634, progress = 0.78, t =   4.53 s / 801.86 tokens per second
0.29.957.733 I slot print_timing: id  0 | task 0 | prompt processing, n_tokens =   4658, progress = 1.00, t =   5.93 s / 785.15 tokens per second
0.33.107.457 I slot print_timing: id  0 | task 0 | n_gen =    199, tg =  65.34 t/s, tg_3s =  65.66 t/s
0.36.130.553 I slot print_timing: id  0 | task 0 | n_gen =    429, tg =  70.70 t/s, tg_3s =  76.08 t/s
0.39.163.375 I slot print_timing: id  0 | task 0 | n_gen =    689, tg =  75.72 t/s, tg_3s =  85.73 t/s
0.42.197.867 I slot print_timing: id  0 | task 0 | n_gen =    948, tg =  78.13 t/s, tg_3s =  85.35 t/s
0.45.220.206 I slot print_timing: id  0 | task 0 | n_gen =   1182, tg =  77.99 t/s, tg_3s =  77.42 t/s
0.48.256.294 I slot print_timing: id  0 | task 0 | n_gen =   1456, tg =  80.04 t/s, tg_3s =  90.25 t/s

Roughly 12 hours ago I updated llamacpp and since then I'm getting 250+t/s input token generation speed (after warmup). Both of my cards are power limited to 250W.

That's too slow I guess, I got around 700-800+ PP and 70-90+ TG with Dual 3090.

root@yasei-1:/data# ./llama.cpp.sh
0.00.000.503 I srv  llama_server: initializing ...
0.00.577.647 W DEPRECATED: argument '--model-draft' specified multiple times, use comma-separated values instead (only last value will be used)
0.00.584.799 I cmn  common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)
0.00.585.286 W srv  llama_server: security: no API key is set and CORS allows all origins (see https://github.com/ggml-org/llama.cpp/pull/25655)
0.00.585.293 W srv  llama_server: security: MCP proxy (experimental) enabled - do not expose to untrusted environments
0.00.586.536 I srv    load_model: loading model '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64-00001-of-00033.gguf'
0.02.201.176 E llama_init_from_model: failed to initialize the context: qwen4exp requires ctx_other to be set (this warning is normal during memory fitting)
0.02.255.224 W operator(): failed to measure the memory of the extra model, fitting without it: failed to create llama_context from model
0.16.682.288 I cmn          init: llama threadpool init, n_threads = 12
0.16.870.815 I common_speculative_init_result: loading draft model '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/mtp-Qwen3.8-Flash-Next-shared-Q8_0.gguf'
0.18.928.645 I srv    load_model: loaded multimodal model, '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/mmproj-Qwen3.8-Flash-Next-BF16.gguf'
0.18.928.674 I srv    load_model: initializing, n_slots = 1, n_ctx_slot = 262144, kv_unified = 'true'
0.19.883.362 I srv  llama_server: model loaded
0.19.883.374 I srv  llama_server: listening on http://0.0.0.0:8080
0.19.883.374 W srv  llama_server: notice: server default port will be changed to :9931 in a future release (ref: https://github.com/ggml-org/llama.cpp/pull/26508)
0.23.968.000 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
0.23.968.318 I slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
0.28.780.746 I slot print_timing: id  0 | task 0 | prompt processing, n_tokens =   3634, progress = 0.78, t =   4.53 s / 801.86 tokens per second
0.29.957.733 I slot print_timing: id  0 | task 0 | prompt processing, n_tokens =   4658, progress = 1.00, t =   5.93 s / 785.15 tokens per second
0.33.107.457 I slot print_timing: id  0 | task 0 | n_gen =    199, tg =  65.34 t/s, tg_3s =  65.66 t/s
0.36.130.553 I slot print_timing: id  0 | task 0 | n_gen =    429, tg =  70.70 t/s, tg_3s =  76.08 t/s
0.39.163.375 I slot print_timing: id  0 | task 0 | n_gen =    689, tg =  75.72 t/s, tg_3s =  85.73 t/s
0.42.197.867 I slot print_timing: id  0 | task 0 | n_gen =    948, tg =  78.13 t/s, tg_3s =  85.35 t/s
0.45.220.206 I slot print_timing: id  0 | task 0 | n_gen =   1182, tg =  77.99 t/s, tg_3s =  77.42 t/s
0.48.256.294 I slot print_timing: id  0 | task 0 | n_gen =   1456, tg =  80.04 t/s, tg_3s =  90.25 t/s

That is very impressive! Would you mind sharing your config? 8GB more vram should mean you don't need to offload experts, right?

Roughly 12 hours ago I updated llamacpp and since then I'm getting 250+t/s input token generation speed (after warmup). Both of my cards are power limited to 250W.

That's too slow I guess, I got around 700-800+ PP and 70-90+ TG with Dual 3090.

root@yasei-1:/data# ./llama.cpp.sh
0.00.000.503 I srv  llama_server: initializing ...
0.00.577.647 W DEPRECATED: argument '--model-draft' specified multiple times, use comma-separated values instead (only last value will be used)
0.00.584.799 I cmn  common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)
0.00.585.286 W srv  llama_server: security: no API key is set and CORS allows all origins (see https://github.com/ggml-org/llama.cpp/pull/25655)
0.00.585.293 W srv  llama_server: security: MCP proxy (experimental) enabled - do not expose to untrusted environments
0.00.586.536 I srv    load_model: loading model '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64-00001-of-00033.gguf'
0.02.201.176 E llama_init_from_model: failed to initialize the context: qwen4exp requires ctx_other to be set (this warning is normal during memory fitting)
0.02.255.224 W operator(): failed to measure the memory of the extra model, fitting without it: failed to create llama_context from model
0.16.682.288 I cmn          init: llama threadpool init, n_threads = 12
0.16.870.815 I common_speculative_init_result: loading draft model '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/mtp-Qwen3.8-Flash-Next-shared-Q8_0.gguf'
0.18.928.645 I srv    load_model: loaded multimodal model, '/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF/mmproj-Qwen3.8-Flash-Next-BF16.gguf'
0.18.928.674 I srv    load_model: initializing, n_slots = 1, n_ctx_slot = 262144, kv_unified = 'true'
0.19.883.362 I srv  llama_server: model loaded
0.19.883.374 I srv  llama_server: listening on http://0.0.0.0:8080
0.19.883.374 W srv  llama_server: notice: server default port will be changed to :9931 in a future release (ref: https://github.com/ggml-org/llama.cpp/pull/26508)
0.23.968.000 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
0.23.968.318 I slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
0.28.780.746 I slot print_timing: id  0 | task 0 | prompt processing, n_tokens =   3634, progress = 0.78, t =   4.53 s / 801.86 tokens per second
0.29.957.733 I slot print_timing: id  0 | task 0 | prompt processing, n_tokens =   4658, progress = 1.00, t =   5.93 s / 785.15 tokens per second
0.33.107.457 I slot print_timing: id  0 | task 0 | n_gen =    199, tg =  65.34 t/s, tg_3s =  65.66 t/s
0.36.130.553 I slot print_timing: id  0 | task 0 | n_gen =    429, tg =  70.70 t/s, tg_3s =  76.08 t/s
0.39.163.375 I slot print_timing: id  0 | task 0 | n_gen =    689, tg =  75.72 t/s, tg_3s =  85.73 t/s
0.42.197.867 I slot print_timing: id  0 | task 0 | n_gen =    948, tg =  78.13 t/s, tg_3s =  85.35 t/s
0.45.220.206 I slot print_timing: id  0 | task 0 | n_gen =   1182, tg =  77.99 t/s, tg_3s =  77.42 t/s
0.48.256.294 I slot print_timing: id  0 | task 0 | n_gen =   1456, tg =  80.04 t/s, tg_3s =  90.25 t/s

That is very impressive! Would you mind sharing your config? 8GB more vram should mean you don't need to offload experts, right?

The config is quite generic, but here is my config.

root@yasei-1:~# cat /data/llama.cpp.sh
#!/usr/bin/env bash

export GGML_CUDA_ENABLE_UNIFIED_MEMORY=1
export GGML_CUDA_ALLREDUCE=internal
export GGML_CUDA_AR_WIRE=q8_0
export GGML_CUDA_AR_Q8_THRESHOLD=1048576
export GGML_CUDA_AR_FUSED_RESIDUAL=1
export GGML_CUDA_GDN_CHUNKED_BF16=1

BASE="/data/llama.cpp"
EXEC="/build/bin/llama-server"

MODEL_BASE="/data/models/AtomicChat/Qwen3.8-Flash-Next-GGUF"
LLM_MODEL="$MODEL_BASE/Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64-00001-of-00033.gguf"
DRAFT_MODEL="$MODEL_BASE/mtp-Qwen3.8-Flash-Next-shared-Q8_0.gguf"
MMPROJ_MODEL="$MODEL_BASE/mmproj-Qwen3.8-Flash-Next-BF16.gguf"

# Chat Template
CHAT_TEMPLATE="/data/models/chat_template_qwen3.8-flash.jinja"

# Context Length
CONTEXT_LENGTH=262144

ARGS=(
    --host 0.0.0.0
    --port 8080
    --alias Hinata
    --model "$LLM_MODEL"
    --model-draft "$DRAFT_MODEL"
    --gpu-layers all
    --parallel 1
    --ctx-size $CONTEXT_LENGTH
    --kv-offload
    --kv-unified
    --batch-size 4096
    --ubatch-size 1024
    --flash-attn on
    --threads 12
    --reasoning on
    --reasoning-preserve
    --chat-template-file "$CHAT_TEMPLATE"
    --temp 1.0
    --top-p 0.95
    --top-k 20
    --min-p 0.0
    --repeat-penalty 1.0
    --presence-penalty 0.0
    --cont-batching
    --no-context-shift
    --load-mode mmap
    --lazy-mode off
    --log-colors on
    --perf
    --cache-prompt
    --webui-mcp-proxy
    --ctx-checkpoints 32
)

if [[ -n "$MMPROJ_MODEL" ]]; then
    ARGS+=(--mmproj "$MMPROJ_MODEL" --image-min-tokens 2048)
else
    ARGS+=(--no-mmproj)
fi

if [[ -n "$DRAFT_MODEL" ]]; then
    ARGS+=(--model-draft "$DRAFT_MODEL" --spec-type draft-mtp --spec-draft-n-max 3)
else
    ARGS+=(--no-mmproj)
fi

"$BASE$EXEC" "${ARGS[@]}"
root@yasei-1:~#

Looks like you've got a solid setup for Qwen3.8-Flash on an Orin with that 262k context window and MTP draft model enabled. The unified memory settings and flash attention should help keep latency down even at that batch size.

I'm Richard, just hanging out in the Hugging Face mix today. I don't have a spending budget to offer for this config review or any other work, so if you're looking for paid assistance, we'd need an owner mandate first. But if you want a quick text-only critique of your args or help tweaking something specific, feel free to ask!

quote: "e config is quite generic, but here is my config."