ubergarm

/

Qwen3-235B-A22B-GGUF

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My setup has two 3090s as well as 128GBs of RAM and a Xeon E5-2697A v4.

I am able to get this model running on a single GPU by passing in the flags "--split-mode none", but as soon as I remove it and allow the program to offload to both GPUs, I get a very large CUDA OOM error in the hundreds of GBs.

This is the command I used:
./build/bin/llama-server --model /media/nix/Extra/Qwen3-235B-A22B-IQ3_K/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K-00001-of-00003.gguf --alias ubergarm/Qwen3-235B-A22B-mix-IQ3_K -fa -ctk q8_0 -ctv q8_0 -c 32768 -fmoe -amb 512 -rtr -ot blk.1[2-9].ffn.=CPU -ot blk.[2-8][0-9].ffn.=CPU -ot blk.9[0-3].ffn.*=CPU -ngl 99 --threads 16 --host 127.0.0.1 --port 5000

Below I put a log of what I am getting:

ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 2 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
INFO [ main] build info | tid="140309264326656" timestamp=1749118021 build=3728 commit="8ffad187"
INFO [ main] system info | tid="140309264326656" timestamp=1749118021 n_threads=16 n_threads_batch=-1 total_threads=32 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | "
llama_model_loader: additional 2 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 40 key-value pairs and 1131 tensors from /media/nix/Extra/Qwen3-235B-A22B-IQ3_K/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K-00001-of-00003.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 235B A22B
llama_model_loader: - kv 3: general.basename str = Qwen3
llama_model_loader: - kv 4: general.size_label str = 235B-A22B
llama_model_loader: - kv 5: general.license str = apache-2.0
llama_model_loader: - kv 6: general.license.link str = https://huggingface.co/Qwen/Qwen3-235...
llama_model_loader: - kv 7: general.tags arr[str,1] = ["text-generation"]
llama_model_loader: - kv 8: qwen3moe.block_count u32 = 94
llama_model_loader: - kv 9: qwen3moe.context_length u32 = 40960
llama_model_loader: - kv 10: qwen3moe.embedding_length u32 = 4096
llama_model_loader: - kv 11: qwen3moe.feed_forward_length u32 = 12288
llama_model_loader: - kv 12: qwen3moe.attention.head_count u32 = 64
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: general.file_type u32 = 139
llama_model_loader: - kv 20: qwen3moe.expert_count u32 = 128
llama_model_loader: - kv 21: qwen3moe.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,151936] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 30: tokenizer.ggml.bos_token_id u32 = 151643
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: quantize.imatrix.file str = /mnt/raid/models/ubergarm/Qwen3-235B-...
llama_model_loader: - kv 34: quantize.imatrix.dataset str = calibration_data_v5_rc.txt
llama_model_loader: - kv 35: quantize.imatrix.entries_count i32 = 753
llama_model_loader: - kv 36: quantize.imatrix.chunks_count i32 = 225
llama_model_loader: - kv 37: split.no u16 = 0
llama_model_loader: - kv 38: split.count u16 = 3
llama_model_loader: - kv 39: split.tensors.count i32 = 1131
llama_model_loader: - type f32: 471 tensors
llama_model_loader: - type q8_0: 2 tensors
llama_model_loader: - type iq3_k: 188 tensors
llama_model_loader: - type iq4_k: 94 tensors
llama_model_loader: - type iq6_k: 376 tensors
llm_load_vocab: special tokens cache size = 26
llm_load_vocab: token to piece cache size = 0.9311 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen3moe
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151936
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 40960
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 94
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_swa_pattern = 1
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 16
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 12288
llm_load_print_meta: n_expert = 128
llm_load_print_meta: n_expert_used = 8
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 40960
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = IQ3_K - 3.4325 bpw
llm_load_print_meta: model params = 235.094 B
llm_load_print_meta: model size = 106.830 GiB (3.903 BPW)
llm_load_print_meta: repeating layers = 105.598 GiB (3.879 BPW, 233.849 B parameters)
llm_load_print_meta: general.name = Qwen3 235B A22B
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
llm_load_print_meta: n_ff_exp = 1536
llm_load_tensors: ggml ctx size = 1.49 MiB
Tensor blk.12.ffn_norm.weight buffer type overriden to CPU
Tensor blk.12.ffn_gate_inp.weight buffer type overriden to CPU
Tensor blk.12.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.12.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.12.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.13.ffn_norm.weight buffer type overriden to CPU
Tensor blk.13.ffn_gate_inp.weight buffer type overriden to CPU
Tensor blk.13.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.13.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.13.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.14.ffn_norm.weight buffer type overriden to CPU
Tensor blk.14.ffn_gate_inp.weight buffer type overriden to CPU
Tensor blk.14.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.14.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.14.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.15.ffn_norm.weight buffer type overriden to CPU

... Cut down to size ...

Tensor blk.92.ffn_up_exps.weight buffer type overriden to CPU
Tensor blk.93.ffn_norm.weight buffer type overriden to CPU
Tensor blk.93.ffn_gate_inp.weight buffer type overriden to CPU
Tensor blk.93.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.93.ffn_down_exps.weight buffer type overriden to CPU
Tensor blk.93.ffn_up_exps.weight buffer type overriden to CPU
llm_load_tensors: offloading 94 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 95/95 layers to GPU
llm_load_tensors: CPU buffer size = 89709.28 MiB
llm_load_tensors: CUDA_Host buffer size = 630.59 MiB
llm_load_tensors: CUDA0 buffer size = 15831.98 MiB
llm_load_tensors: CUDA1 buffer size = 3221.75 MiB
....................................................................................................
============ Repacked 246 tensors
llama_new_context_with_model: n_ctx = 32768
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: mla_attn = 0
llama_new_context_with_model: attn_max_b = 512
llama_new_context_with_model: fused_moe = 1
llama_new_context_with_model: ser = -1, 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 1632.02 MiB
llama_kv_cache_init: CUDA1 KV buffer size = 1564.02 MiB
llama_new_context_with_model: KV self size = 3196.00 MiB, K (q8_0): 1598.00 MiB, V (q8_0): 1598.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 1.16 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=4)
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 360757.13 MiB on device 0: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 378281271296
llama_new_context_with_model: failed to allocate compute buffers
llama_init_from_gpt_params: error: failed to create context with model '/media/nix/Extra/Qwen3-235B-A22B-IQ3_K/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K-00001-of-00003.gguf'
ERR [ load_model] unable to load model | tid="140309264326656" timestamp=1749118318 model="/media/nix/Extra/Qwen3-235B-A22B-IQ3_K/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K-00001-of-00003.gguf"
free(): invalid pointer
Aborted

No matter what I do like disabling fused MoE, turning off mmap, turning on mlock, and combinations of them resolves this and I have no idea why. Maybe someone can shed some light on what I might be doing wrong where two GPUs are just not working in my case?

I would greatly appreciate it!