Unsloth quants available:

▲ genpfault 2 hours ago | parent | next [-]

Getting ~36-33 tok/s (see the "S_TG t/s" column) on a 24GB Radeon RX 7900 XTX using llama.cpp's Vulkan backend:

    $ llama-server --version
    version: 8851 (e365e658f)

    $ llama-batched-bench -hf unsloth/Qwen3.6-27B-GGUF:IQ4_XS -npp 1000,2000,4000,8000,16000,32000 -ntg 128 -npl 1 -c 34000
    |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
    |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
    |  1000 |    128 |    1 |   1128 |    1.529 |   654.11 |    3.470 |    36.89 |    4.999 |   225.67 |
    |  2000 |    128 |    1 |   2128 |    3.064 |   652.75 |    3.498 |    36.59 |    6.562 |   324.30 |
    |  4000 |    128 |    1 |   4128 |    6.180 |   647.29 |    3.535 |    36.21 |    9.715 |   424.92 |
    |  8000 |    128 |    1 |   8128 |   12.477 |   641.16 |    3.582 |    35.73 |   16.059 |   506.12 |
    | 16000 |    128 |    1 |  16128 |   25.849 |   618.98 |    3.667 |    34.91 |   29.516 |   546.42 |
    | 32000 |    128 |    1 |  32128 |   57.201 |   559.43 |    3.825 |    33.47 |   61.026 |   526.47 |

▲ johndough an hour ago | parent [-]

Getting ~44-40 tok/s on 24GB RTX 3090 (llama.cpp version 8884, same llama-batched-bench call):

    |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
    |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
    |  1000 |    128 |    1 |   1128 |    0.684 |  1462.61 |    2.869 |    44.61 |    3.553 |   317.47 |
    |  2000 |    128 |    1 |   2128 |    1.390 |  1438.84 |    2.868 |    44.64 |    4.258 |   499.80 |
    |  4000 |    128 |    1 |   4128 |    2.791 |  1433.18 |    2.886 |    44.35 |    5.677 |   727.11 |
    |  8000 |    128 |    1 |   8128 |    5.646 |  1416.98 |    2.922 |    43.80 |    8.568 |   948.65 |
    | 16000 |    128 |    1 |  16128 |   11.851 |  1350.10 |    3.007 |    42.57 |   14.857 |  1085.51 |
    | 32000 |    128 |    1 |  32128 |   25.855 |  1237.66 |    3.168 |    40.40 |   29.024 |  1106.96 |

Edit: Model gets stuck in infinite loops at this quantization level. I've also tried Q5_K_M quantization (fits up to 51968 context length), which seems more robust.

▲ GrinningFool 2 hours ago | parent | prev | next [-]

128GB (112 GB avail) Strix AI 395+ Radeon 8060x (gfx1151)

llama-* version 8889 w/ rocm support ; nightly rocm

llama.cpp/build/bin/llama-batched-bench --version unsloth/Qwen3.6-27B-GGUF:UD-Q8_K_XL -npp 1000,2000,4000,8000,16000,32000 -ntg 128 -npl 1 -c 34000

    |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
    |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
    |  1000 |    128 |    1 |   1128 |    2.776 |   360.22 |   20.192 |     6.34 |   22.968 |    49.11 |
    |  2000 |    128 |    1 |   2128 |    5.778 |   346.12 |   20.211 |     6.33 |   25.990 |    81.88 |
    |  4000 |    128 |    1 |   4128 |   11.723 |   341.22 |   20.291 |     6.31 |   32.013 |   128.95 |
    |  8000 |    128 |    1 |   8128 |   24.223 |   330.26 |   20.399 |     6.27 |   44.622 |   182.15 |
    | 16000 |    128 |    1 |  16128 |   52.521 |   304.64 |   20.669 |     6.19 |   73.190 |   220.36 |
    | 32000 |    128 |    1 |  32128 |  120.333 |   265.93 |   21.244 |     6.03 |  141.577 |   226.93 |

More directly comparable to the results posted by genpfault (IQ4_XS):

llama.cpp/build/bin/llama-batched-bench -hf unsloth/Qwen3.6-27B-GGUF:IQ4_XS -npp 1000,2000,4000,8000,16000,32000 -ntg 128 -npl 1 -c 34000

    |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
    |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
    |  1000 |    128 |    1 |   1128 |    2.543 |   393.23 |    9.829 |    13.02 |   12.372 |    91.17 |
    |  2000 |    128 |    1 |   2128 |    5.400 |   370.36 |    9.891 |    12.94 |   15.291 |   139.17 |
    |  4000 |    128 |    1 |   4128 |   10.950 |   365.30 |    9.972 |    12.84 |   20.922 |   197.31 |
    |  8000 |    128 |    1 |   8128 |   22.762 |   351.46 |   10.118 |    12.65 |   32.880 |   247.20 |
    | 16000 |    128 |    1 |  16128 |   49.386 |   323.98 |   10.387 |    12.32 |   59.773 |   269.82 |
    | 32000 |    128 |    1 |  32128 |  114.218 |   280.16 |   10.950 |    11.69 |  125.169 |   256.68 |

▲

amstan an hour ago | parent [-]

you should try vulkan instead of rocm. it goes like 20% faster.

	▲	MrDrMcCoy 21 minutes ago \| parent [-]
		Is that based on recent experience? With "stable" ROCm, or the (IMHO better) releases from TheRock? With older or more recent hardware? The AMD landscape is rather uneven.

▲ endymi0n 3 hours ago | parent | prev [-]

at this trajectory, unsloth are going to release the models BEFORE the model drop within the next weeks...

	▲	danielhanchen 2 hours ago \| parent [-]
		Haha :)