Segment matmul from jax-md, Pallas TPU
Google TPU v5e
Pass, 65 of 65 shapes. Port median 161.4 us against 208.4 us for the fallback
Explore recorded kernel results, choose a target, and carry that context into your Engine project.
49 of 49 recorded results
Reset filtersGoogle TPU v5e
Pass, 65 of 65 shapes. Port median 161.4 us against 208.4 us for the fallback
Google TPU v6e
Pass, 65 of 65 shapes. Port median 155.4 us against 176.7 us for the JAX reference on the production shape
NVIDIA H100 80GB HBM3
Pass. Picked the current kernel, output matches the reference on 16,777,216 values
NVIDIA Tesla T4
Pass for the recorded test inputs. Median 79.552 us, p99 137.248 us over 20,000 calls
NVIDIA Tesla T4
Pass. Best candidate 1.124 ms and 76.7 mJ a call, against 3.911 ms and 276.0 mJ for eager PyTorch
Qualcomm QCS6490, Dragonwing RB3 Gen 2
Fail on per value correctness, as recorded. Latency and peak memory pass. int8 p50 402 us, int16 p50 446 us
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 65 of 65 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 69 of 69 shapes
Google TPU v4, v5e, v5p, v6e through Pallas Mosaic
Pass, 69 of 69 shapes
AWS Trainium2, AWS Trainium1, AWS Inferentia2
Pass, 69 of 69 shapes
AMD Instinct CDNA, wave64, AMD Radeon RDNA, wave32
Pass, 69 of 69 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 72 of 72 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 72 of 72 shapes
Google TPU through Pallas Mosaic
Pass, 72 of 72 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 65 of 65 shapes
Google TPU through Pallas Mosaic
Pass, 65 of 65 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 80 of 80 shapes
Google TPU through Pallas Mosaic
Pass, 80 of 80 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 65 of 65 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 69 of 69 shapes
Google TPU through Pallas
Pass, 69 of 69 shapes
AWS Trainium3 through NKI
Pass, 69 of 69 shapes
AMD Instinct through HIP
Pass, 69 of 69 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 68 of 68 shapes
Google TPU through Pallas Mosaic
Pass, 68 of 68 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 65 of 65 shapes
Google TPU through Pallas Mosaic
Pass, 65 of 65 shapes
AMD Instinct MI300 through the Triton ROCm backend
Pass, 69 of 69 shapes
NVIDIA through the Triton CUDA backend, AMD through the Triton ROCm backend
Pass, 75 of 75 shapes
AWS Trainium trn1
Pass, 4 of 4 rounds. 4,096 of 4,096 bins matched over 6 device runs
AMD Instinct MI300X
Pass, 5 of 5 rounds. 0.1015 ms against a 86.3898 ms baseline
Measured on real GPUs lists results measured on Tesla T4 with the chip, driver, numbers and conditions of each run.
Filter public library records by library, version, chip and outcome. Measured records describe recorded attempts. Reported records describe outside sources. Each result guides a fresh Verify on your configuration.
100 library records shown (up to 100 matches)
Reset library filtersChip unspecified
github issue
reported fix: AMDGPU MIR serialization missing handling of MinNumAGPRs
Source evidence →Chip unspecified
github issue
closed without a linked fix: [Performance]: 265k NPU much slower than CPU at LLM inference
Source evidence →Chip unspecified
github issue
reported fix: ReordererSequenceParallel (ETP=1) doesn't work with torch.compile + AC
Source evidence →Chip unspecified
github issue
closed without a linked fix: Misc. bug: Slower performance on newer models Apriel-1.5-15b, granite-4.0-h-tiny
Source evidence →Chip unspecified
github issue
open issue: syzbot, syz-cluster: avoid duplication of base syzkaller configs
Source evidence →Chip unspecified
github issue
open issue: Support for Qwen3-VL models
Source evidence →Chip unspecified / gfx950
github issue
open issue: [Codegen] Buffer read across devices instead of copied
Source evidence →Chip unspecified
github issue
closed without a linked fix: [Performance]: Use int over list[int] as output_tokens to reduce GC overhead
Source evidence →Chip unspecified / sm_100
github issue
open issue: [CuteDSL] ICE for flash combine kernel on blackwell
Source evidence →Chip unspecified
github issue
closed without a linked fix: Fields of anonymous structures in arrays can't be accessed (BTF)
Source evidence →H100
github issue
closed without a linked fix: [AMD] Validate perf numbers on AMD MI350
Source evidence →Chip unspecified
github issue
open issue: NVRM: memmgrRestorePowerMgmtState_KERNEL: !!!!! Calling Resume on an active GPU or the previous Suspend call might have failed !!!!!!
Source evidence →Chip unspecified
github issue
open issue: [Codegen][GPU] Refactor and consolidate padding passes and configurations
Source evidence →B200
github issue
open issue: CUDNN jax.nn.dot_product_attention fails on CUDA 13
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_logging is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_metrics is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_report_handler is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_result is flaky
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_serialization is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_storage is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_sync_actor is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_thread_runner is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_torch_trainer is flaky
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_util is flaky
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_validation_manager is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_worker is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_worker_group_poll_status is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/tune:test_actor_reuse is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:lightning_exp_tracking_mlflow is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:lightning_exp_tracking_tensorboard is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:mlflow_simple_example is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_api_migrations is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_checkpoint is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_checkpoint_manager is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_e2e_wandb_integration is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_lightgbm_predictor is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_predictor is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_session is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_storage is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_tensorflow_checkpoint is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_torch_checkpoint is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_torch_utils is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_train_head is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_utils is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_xgboost_predictor is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_state_export is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_telemetry is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:tensorflow_regression_example is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_base_trainer is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/tune:test_actor_caching is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://doc:doctest[train] is consistently_failing
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train:test_torch_predictor is flaky
Source evidence →Chip unspecified
github issue
closed without a linked fix: Thread Usage Limitation in OpenBLAS on Windows on ARM64 Setup
Source evidence →Chip unspecified
github issue
closed without a linked fix: CI test linux://python/ray/train/v2:test_torch_gpu is flaky
Source evidence →Chip unspecified
github issue
closed without a linked fix: Evaluating Whisper Large V3 Concurrency on TensorRT Deployment
Source evidence →Chip unspecified
github issue
closed without a linked fix: [bug][relax.frontend.torch] FFI segfault in tvm::relax::Tuple::Tuple when importing torch.export graph with 4D advanced-indexing write (aten.index_put_) and tuple outputs
Source evidence →Chip unspecified
github issue
closed without a linked fix: Support 32-bit ARM (armeabi-v7a) ABI for LiteRT Next on legacy Android devices
Source evidence →Chip unspecified
github issue
closed without a linked fix: [QST] Why sparse flashmla prefill only support batch_size=1?
Source evidence →Chip unspecified
github issue
closed without a linked fix: CD daemon teardown may leave behind stale IMEX daemon (`Address already in use`)
Source evidence →Chip unspecified
github issue
closed without a linked fix: Multi-turn tool use RL training with SGLang has incorrect response trajectory
Source evidence →Chip unspecified
github issue
closed without a linked fix: [Bug] Importing `sgl_kernel` now hard-requires CUDA toolkit (regression vs `v0.5.1`)
Source evidence →Chip unspecified
github issue
reported fix: [AMDGPU] `sort` in `AMDGPURewriteAGPRCopyMFMAImpl::eliminateSpillsOfReassignedVGPRs()` needs to have strict weak ordering
Source evidence →Chip unspecified
github issue
open issue: System Mediated MPS-sharing of GPUs
Source evidence →Chip unspecified
github issue
closed without a linked fix: Publish DeviceTaints to report / track GPU health
Source evidence →Chip unspecified
github issue
closed without a linked fix: Automatically select training micro batch size based on GPU memory
Source evidence →H100
github issue
closed without a linked fix: Multi-node NCCL performance drop due to cross numa issues
Source evidence →Chip unspecified
github issue
closed without a linked fix: Python backend stub is becoming zombie process
Source evidence →Chip unspecified
github issue
open issue: How to quantize both vision encoder and llm together?
Source evidence →H20
github issue
closed without a linked fix: [Bug] Server hang during Kimi-K2 serving ( KV-Cache flushed)
Source evidence →A100
github issue
open issue: NCCL Broadcast Timeout in Megatron-LM Training of Qwen3-235B with Multi-Turn Tool Calling (max_turns=5)
Source evidence →Chip unspecified
github issue
closed without a linked fix: [Bug] flashinfer v0.4.0 jit failure
Source evidence →B200
github issue
closed without a linked fix: Trying to understand performance
Source evidence →Chip unspecified
github issue
closed without a linked fix: [LiteRT Play Services] inference on NPU
Source evidence →Chip unspecified
github issue
open issue: Could not find any implementation for node {ForeignNode[/model/Unsqueeze + /model/Unsqueeze_1.../Cast]} failure of TensorRT 10.13.3 when running trtexec on GPU 3090
Source evidence →Chip unspecified
github issue
closed without a linked fix: bug: segfault on q4_0 repacking on windows avx2 cpu only
Source evidence →Chip unspecified
github issue
closed without a linked fix: Query on performance-modeling of MoE Grouped GEMM
Source evidence →B200
github issue
closed without a linked fix: [Bug]: EXAONE 4.0 FP8 trtllm-serve failure
Source evidence →Chip unspecified
github issue
closed without a linked fix: [pytorch upstream] softmax bwd on BMG is slow
Source evidence →Chip unspecified
github issue
closed without a linked fix: More robust distributed loss
Source evidence →Chip unspecified
github issue
open issue: Can DCGM independently view the health information of the CPU?
Source evidence →Chip unspecified
github issue
reported fix: dashboard: more graceful error handling for user commands
Source evidence →Chip unspecified
github issue
open issue: Missing sparse backward kernel.
Source evidence →Chip unspecified
github issue
closed without a linked fix: Any example for static fp8 quantization?
Source evidence →Chip unspecified
github issue
open issue: Trouble loading HSMP module into Kernel
Source evidence →Chip unspecified
github issue
closed without a linked fix: Can't using llmcompressor for awq quantization and using autoawq completely replace each other?
Source evidence →Chip unspecified
github issue
open issue: segfault due to thread-unsafe getenv() call
Source evidence →Chip unspecified
github issue
closed without a linked fix: [AutoDeploy]: Improve MoE performance for TP>1
Source evidence →Chip unspecified
github issue
open issue: [Feature]: FP8 for DS-R1
Source evidence →Chip unspecified
github issue
closed without a linked fix: Accuracy issue with MALSD decoding with LM fusion
Source evidence →Chip unspecified
github issue
open issue: Empty pipeline stages deadlock with custom pipeline-parallel-layout
Source evidence →B200
github issue
closed without a linked fix: [Performance]: Deepseek3.2 Compile Time Regression on B200 with Pytorch 2.9
Source evidence →Chip unspecified / gfx950
github issue
open issue: [gfx950][mxfp4] Inefficient codegen for undersized tiles
Source evidence →Chip unspecified
github issue
closed without a linked fix: JIT install in non-editable mode doesn't work?
Source evidence →Chip unspecified
github issue
closed without a linked fix: vllm sleep mode patch for AMD GPU no longer works
Source evidence →T4
github issue
reported fix: [BUG] CUDA conv fails to find execution plan
Source evidence →Chip unspecified
github issue
closed without a linked fix: regression attaching tracepoint:syscalls:sys_enter_landlock_add_rule in v0.24 (field has incomplete type)
Source evidence →GB200
github issue
closed without a linked fix: Seeing a performance drop when running Nemotron 340B workload using container: nvcr.io/nvidia/nemo:25.09 compared to when using container nvcr.io/nvidia/nemo:25.07.01 on GB200 and B200 systems.
Source evidence →B200
github issue
open issue: can ncu profiling peer memory traffic
Source evidence →H20
github issue
closed without a linked fix: vllm hang when use lora with qwen2.5-72b
Source evidence →Chip unspecified
github issue
open issue: with clang 21.1.6 as wrapper compiler for cuda-aware openmpi-5.0.9 and compile options -fopenmp and -fopenmp-targets=nvptx64-nvidia-cuda one gets the message "The call to cuMemHostRegister(0x7fb38c000000, 134217728, 0) failed." for every code with OpenMPI [Patch is proposed as temporary solution in the last comment]
Source evidence →Explore 46 maker entries across cloud, data centre and edge. Register a specific chip for qualification; availability and support are confirmed during scoping.
RDU, SN40L
Request this target →Instinct MI300X, MI325X, MI355X
Request this target →H100, B200, A100, L4, T4
Request this target →Trainium, Inferentia2
Request this target →TPU v5e, v6e
Request this target →Gaudi 2 and 3
Request this target →Cloud AI 100
Request this target →Wormhole, Blackhole
Request this target →WSE-3
Request this target →LPU
Request this target →Corsair
Request this target →RNGD
Request this target →ATOM, REBEL
Request this target →Sohu
Request this target →MatX chip
Request this target →IPU
Request this target →Atlas
Request this target →Memory and compute on one die
Request this target →Hard wired model silicon
Request this target →Data centre inference chips
Request this target →EN100
Request this target →Maverick 2
Request this target →Spatial dataflow
Request this target →Maia
Request this target →MTIA
Request this target →Snapdragon, Hexagon, Dragonwing
Request this target →Jetson Orin, Thor
Request this target →MLSoC, Modalix
Request this target →Metis
Request this target →CV, N1
Request this target →Hailo-8, 8L, 10
Request this target →Exynos NPU
Request this target →GSP
Request this target →Neural Engine, M GPU
Request this target →NeuroPilot NPUs
Request this target →DX-M1
Request this target →KL520 to KL730
Request this target →SAKURA-II
Request this target →Akida
Request this target →Chimera GPNPU
Request this target →i.MX, eIQ
Request this target →RZ, DRP-AI
Request this target →TDA4, AM6x
Request this target →Ethos NPU, Mali
Request this target →Astra
Request this target →NDP
Request this target →