./llama-server --model '/home/rocm/AI/Qwen3-30B-A3B-128K-UD-Q8_K_XL.gguf' -ngl 999 --ctx-size 32768 -fa --port 8040 -sm row

./llama-server --model '/home/rocm/AI/Qwen3-30B-A3B-128K-UD-Q8_K_XL.gguf' -ngl 999 --ctx-size 32768 -fa --port 8040 -sm row -ub 128

./llama-server --model '/media/rocm/edisk/a/AI/models/Mistral-Small-Instruct-2409-Q8_0.gguf' -ngl 999 --ctx-size 32768 -fa --port 8040 -sm row

./llama-server --model '/media/rocm/edisk/a/AI/models/Mistral-Small-Instruct-2409-Q8_0.gguf' -ngl 999 --ctx-size 32768 -fa --port 8040 -sm row -ub 128

HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)"     cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx906 -DCMAKE_BUILD_TYPE=Release     && cmake --build build --config Release -- -j 12`

Tell me a random fun fact about the Roman Empire 
Qwen3, regardless of ub set to 128 or not: GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG 

Mistral Small 2, regardless of ub set to 128 or not: ialize torsoleialize frameializeializeializeialize uniqueializeialize Art frame uniqueializeialize uniqueialize Univers frameialize autoializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeializeialize

./llama-server --model '/home/rocm/AI/Qwen3-30B-A3B-128K-UD-Q8_K_XL.gguf' -ngl 999 --ctx-size 32768 -fa --port 8040 -sm row
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 ROCm devices:
  Device 0: AMD Radeon Graphics, gfx906:sramecc+:xnack- (0x906), VMM: no, Wave Size: 64
  Device 1: AMD Radeon Graphics, gfx906:sramecc+:xnack- (0x906), VMM: no, Wave Size: 64
build: 5384 (4696d567) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
system info: n_threads = 6, n_threads_batch = 6, total_threads = 12

system_info: n_threads = 6 (n_threads_batch = 6) / 12 | ROCm : NO_VMM = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 | 

main: binding port with default address family
main: HTTP server is listening, hostname: 127.0.0.1, port: 8040, http threads: 11
main: loading model
srv    load_model: loading model '/home/rocm/AI/Qwen3-30B-A3B-128K-UD-Q8_K_XL.gguf'
llama_model_load_from_file_impl: using device ROCm0 (AMD Radeon Graphics) - 32732 MiB free
llama_model_load_from_file_impl: using device ROCm1 (AMD Radeon Graphics) - 32732 MiB free
llama_model_loader: loaded meta data with 39 key-value pairs and 579 tensors from /home/rocm/AI/Qwen3-30B-A3B-128K-UD-Q8_K_XL.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = qwen3moe
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3-30B-A3B-128K
llama_model_loader: - kv   3:                           general.finetune str              = 128k
llama_model_loader: - kv   4:                           general.basename str              = Qwen3-30B-A3B-128K
llama_model_loader: - kv   5:                       general.quantized_by str              = Unsloth
llama_model_loader: - kv   6:                         general.size_label str              = 30B-A3B
llama_model_loader: - kv   7:                           general.repo_url str              = https://huggingface.co/unsloth
llama_model_loader: - kv   8:                       qwen3moe.block_count u32              = 48
llama_model_loader: - kv   9:                    qwen3moe.context_length u32              = 131072
llama_model_loader: - kv  10:                  qwen3moe.embedding_length u32              = 2048
llama_model_loader: - kv  11:               qwen3moe.feed_forward_length u32              = 6144
llama_model_loader: - kv  12:              qwen3moe.attention.head_count u32              = 32
llama_model_loader: - kv  13:           qwen3moe.attention.head_count_kv u32              = 4
llama_model_loader: - kv  14:                    qwen3moe.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  15:  qwen3moe.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  16:                 qwen3moe.expert_used_count u32              = 8
llama_model_loader: - kv  17:              qwen3moe.attention.key_length u32              = 128
llama_model_loader: - kv  18:            qwen3moe.attention.value_length u32              = 128
llama_model_loader: - kv  19:                      qwen3moe.expert_count u32              = 128
llama_model_loader: - kv  20:        qwen3moe.expert_feed_forward_length u32              = 768
llama_model_loader: - kv  21:                 qwen3moe.rope.scaling.type str              = yarn
llama_model_loader: - kv  22:               qwen3moe.rope.scaling.factor f32              = 4.000000
llama_model_loader: - kv  23: qwen3moe.rope.scaling.original_context_length u32              = 32768
llama_model_loader: - kv  24:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  25:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  26:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  28:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 151654
llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  33:               general.quantization_version u32              = 2
llama_model_loader: - kv  34:                          general.file_type u32              = 7
llama_model_loader: - kv  35:                      quantize.imatrix.file str              = Qwen3-30B-A3B-128K-GGUF/imatrix_unslo...
llama_model_loader: - kv  36:                   quantize.imatrix.dataset str              = unsloth_calibration_Qwen3-30B-A3B-128...
llama_model_loader: - kv  37:             quantize.imatrix.entries_count i32              = 384
llama_model_loader: - kv  38:              quantize.imatrix.chunks_count i32              = 685
llama_model_loader: - type  f32:  241 tensors
llama_model_loader: - type q8_0:  263 tensors
llama_model_loader: - type bf16:   75 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 33.51 GiB (9.43 BPW) 
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch             = qwen3moe
print_info: vocab_only       = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 2048
print_info: n_layer          = 48
print_info: n_head           = 32
print_info: n_head_kv        = 4
print_info: n_rot            = 128
print_info: n_swa            = 0
print_info: n_swa_pattern    = 1
print_info: n_embd_head_k    = 128
print_info: n_embd_head_v    = 128
print_info: n_gqa            = 8
print_info: n_embd_k_gqa     = 512
print_info: n_embd_v_gqa     = 512
print_info: f_norm_eps       = 0.0e+00
print_info: f_norm_rms_eps   = 1.0e-06
print_info: f_clamp_kqv      = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale    = 0.0e+00
print_info: f_attn_scale     = 0.0e+00
print_info: n_ff             = 6144
print_info: n_expert         = 128
print_info: n_expert_used    = 8
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 2
print_info: rope scaling     = yarn
print_info: freq_base_train  = 1000000.0
print_info: freq_scale_train = 0.25
print_info: n_ctx_orig_yarn  = 32768
print_info: rope_finetuned   = unknown
print_info: ssm_d_conv       = 0
print_info: ssm_d_inner      = 0
print_info: ssm_d_state      = 0
print_info: ssm_dt_rank      = 0
print_info: ssm_dt_b_c_rms   = 0
print_info: model type       = 30B.A3B
print_info: model params     = 30.53 B
print_info: general.name     = Qwen3-30B-A3B-128K
print_info: n_ff_exp         = 768
print_info: vocab type       = BPE
print_info: n_vocab          = 151936
print_info: n_merges         = 151387
print_info: BOS token        = 11 ','
print_info: EOS token        = 151645 '<|im_end|>'
print_info: EOT token        = 151645 '<|im_end|>'
print_info: PAD token        = 151654 '<|vision_pad|>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
print_info: FIM MID token    = 151660 '<|fim_middle|>'
print_info: FIM PAD token    = 151662 '<|fim_pad|>'
print_info: FIM REP token    = 151663 '<|repo_name|>'
print_info: FIM SEP token    = 151664 '<|file_sep|>'
print_info: EOG token        = 151643 '<|endoftext|>'
print_info: EOG token        = 151645 '<|im_end|>'
print_info: EOG token        = 151662 '<|fim_pad|>'
print_info: EOG token        = 151663 '<|repo_name|>'
print_info: EOG token        = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 48 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 49/49 layers to GPU
load_tensors:  ROCm0_Split model buffer size =   526.88 MiB
load_tensors:  ROCm1_Split model buffer size =  1094.81 MiB
load_tensors:        ROCm0 model buffer size = 16945.42 MiB
load_tensors:        ROCm1 model buffer size = 15156.40 MiB
load_tensors:   CPU_Mapped model buffer size =   593.50 MiB
................................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 32768
llama_context: n_ctx_per_seq = 32768
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = 1
llama_context: freq_base     = 1000000.0
llama_context: freq_scale    = 0.25
llama_context: n_ctx_per_seq (32768) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context:  ROCm_Host  output buffer size =     0.58 MiB
llama_kv_cache_unified: kv_size = 32768, type_k = 'f16', type_v = 'f16', n_layer = 48, can_shift = 1, padding = 256
llama_kv_cache_unified:      ROCm0 KV buffer size =  1600.00 MiB
llama_kv_cache_unified:      ROCm1 KV buffer size =  1472.00 MiB
llama_kv_cache_unified: KV self size  = 3072.00 MiB, K (f16): 1536.00 MiB, V (f16): 1536.00 MiB
llama_context:      ROCm0 compute buffer size =   120.00 MiB
llama_context:      ROCm1 compute buffer size =   300.75 MiB
llama_context:  ROCm_Host compute buffer size =    68.01 MiB
llama_context: graph nodes  = 2935
llama_context: graph splits = 3
common_init_from_params: setting dry_penalty_last_n to ctx_size = 32768
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv          init: initializing slots, n_slots = 1
slot         init: id  0 | task -1 | new slot n_ctx_slot = 32768
main: model loaded
main: chat template, chat_template: {%- if tools %}
    {{- '<|im_start|>system\n' }}
    {%- if messages[0].role == 'system' %}
        {{- messages[0].content + '\n\n' }}
    {%- endif %}
    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
    {%- for tool in tools %}
        {{- "\n" }}
        {{- tool | tojson }}
    {%- endfor %}
    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
    {%- if messages[0].role == 'system' %}
        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
    {%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for forward_message in messages %}
    {%- set index = (messages|length - 1) - loop.index0 %}
    {%- set message = messages[index] %}
    {%- set current_content = message.content if message.content is defined and message.content is not none else '' %}
    {%- set tool_start = '<tool_response>' %}
    {%- set tool_start_length = tool_start|length %}
    {%- set start_of_message = current_content[:tool_start_length] %}
    {%- set tool_end = '</tool_response>' %}
    {%- set tool_end_length = tool_end|length %}
    {%- set start_pos = (current_content|length) - tool_end_length %}
    {%- if start_pos < 0 %}
        {%- set start_pos = 0 %}
    {%- endif %}
    {%- set end_of_message = current_content[start_pos:] %}
    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
        {%- set ns.multi_step_tool = false %}
        {%- set ns.last_query_index = index %}
    {%- endif %}
{%- endfor %}
{%- for message in messages %}
    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
    {%- elif message.role == "assistant" %}
        {%- set m_content = message.content if message.content is defined and message.content is not none else '' %}
        {%- set content = m_content %}
        {%- set reasoning_content = '' %}
        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
            {%- set reasoning_content = message.reasoning_content %}
        {%- else %}
            {%- if '</think>' in m_content %}
                {%- set content = (m_content.split('</think>')|last).lstrip('\n') %}
                {%- set reasoning_content = (m_content.split('</think>')|first).rstrip('\n') %}
                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
            {%- endif %}
        {%- endif %}
        {%- if loop.index0 > ns.last_query_index %}
            {%- if loop.last or (not loop.last and (not reasoning_content.strip() == '')) %}
                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
            {%- else %}
                {{- '<|im_start|>' + message.role + '\n' + content }}
            {%- endif %}
        {%- else %}
            {{- '<|im_start|>' + message.role + '\n' + content }}
        {%- endif %}
        {%- if message.tool_calls %}
            {%- for tool_call in message.tool_calls %}
                {%- if (loop.first and content) or (not loop.first) %}
                    {{- '\n' }}
                {%- endif %}
                {%- if tool_call.function %}
                    {%- set tool_call = tool_call.function %}
                {%- endif %}
                {{- '<tool_call>\n{"name": "' }}
                {{- tool_call.name }}
                {{- '", "arguments": ' }}
                {%- if tool_call.arguments is string %}
                    {{- tool_call.arguments }}
                {%- else %}
                    {{- tool_call.arguments | tojson }}
                {%- endif %}
                {{- '}\n</tool_call>' }}
            {%- endfor %}
        {%- endif %}
        {{- '<|im_end|>\n' }}
    {%- elif message.role == "tool" %}
        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
            {{- '<|im_start|>user' }}
        {%- endif %}
        {{- '\n<tool_response>\n' }}
        {{- message.content }}
        {{- '\n</tool_response>' }}
        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
            {{- '<|im_end|>\n' }}
        {%- endif %}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
    {%- if enable_thinking is defined and enable_thinking is false %}
        {{- '<think>\n\n</think>\n\n' }}
    {%- endif %}
{%- endif %}, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
'
main: server is listening on http://127.0.0.1:8040 - starting the main loop
srv  update_slots: all slots are idle
srv  log_server_r: request: OPTIONS /v1/chat/completions 127.0.0.1 200
srv  params_from_: Chat format: Content-only
slot launch_slot_: id  0 | task 0 | processing task
slot update_slots: id  0 | task 0 | new prompt, n_ctx_slot = 32768, n_keep = 0, n_prompt_tokens = 18
slot update_slots: id  0 | task 0 | kv cache rm [0, end)
slot update_slots: id  0 | task 0 | prompt processing progress, n_past = 18, n_tokens = 18, progress = 1.000000
slot update_slots: id  0 | task 0 | prompt done, n_past = 18, n_tokens = 18
^Csrv    operator(): operator(): cleaning up before exit...
terminate called without an active exception
Aborted (core dumped)