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Graph/memory-capped-inference-oom

Pattern

Memory-Capped Model Inference

memory-capped-inference-oom

Text encoder dequantization and CPU offload still fail because embedding expansion demands large peak RAM, and cgroup/systemd or other environment constraints terminate the process before swap can help.

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Updated7/16/2026

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30 linked nodes

When multiple React components use Zustand selectors like `useStore(state => state.largeDataset)`, does each component end up copying the large dataset into its own memory, increasing total RAM usage?

as soon as RAM utilisation hits ~4-5GB, the child process dies — If I run the fastapi server with workers ( `fastapi start src/api.py --port 3000 --workers 2` ). Tension: The machine has 8 cores and 64GB of RAM. Outcome: The behavior is consistent irrespective of the number of workers ( workers > 1 ).

every model load threw "embedding model could not be loaded: Unable to get model file path or buffer" — worked perfectly on the dev machine but failed in GitHub Actions CI. Tension: causing all embedding-dependent integration tests to fail. Outcome: ingest gate rejecting every node with 'embedding unavailable'.

while running an AWS Lambda function. Tension: The data fetching and processing is good, but the function crashes during the file upload step. Outcome: I’ve tried increasing memory allocation and execution time without success.

the model kept running at a clamped lower tier ("high") — a config setting meant to raise an agent's reasoning/thinking effort. Outcome: the limit was purely in the framework's resolution layer.

Buffer overflow cannot be demonstrated reliably

Software actually aliasing NS and S accesses for RAMs means you can end up with two non-coherent copies of the same data in the caches — secure and non-secure data to be distinguished in caches. Tension: which can desync because of the lack of coherency. Outcome: This is why secure world page tables can be set up with NS=1 pages.

Outcome: The established method to prevent improper memory access is to use processes.

stack layout and register allocation are subject to a great amount of optimization — An attempt to interfere this process nondeterministically. Tension: benchmarks will become useless. Outcome: nondeterministic performance.

I am using kernel 5.14, AMD processor, and do not enable huge pages or transparent huge pages. Tension: my workload is not a multi-tenant cloud environment and there will be no malicious software or attack behavior. Outcome: zeroize pages allocated by the kernel during page fault.

uses fixed-size stack buffers (prefix/suffix/number/new_mnemonic of size 15) — In opcodes/s390-mkopc.c, insertExpandedMnemonic(). Tension: copies/concatenates unbounded substrings from the input mnemonic. Outcome: enabling stack buffer overflow when mnemonics exceed expected length.

complex prompts — e.g. multi-constraint relation/JSON extraction — intermittently time out or run very slowly — Routing an extraction/agent pipeline to Vertex AI Gemini through its OpenAI-compatible endpoint. Tension: Gemini 2.5/3.x "flash" and "pro" are THINKING models: by default they spend a large hidden reasoning budget even on trivial inputs. Outcome: this made ~1/3 of records ship edgeless.

sometimes it fails at new/malloc/delete/free — in usb_otg_fs(interrupt) i read data and dynamically allocate 8 bytes. Tension: the problem is sometimes it fails at new/malloc/delete/free.

Dynamic memory allocation is ill-advised in any event in most small scale and real-time embedded applications — in most small scale and real-time embedded applications. Tension: non-deterministic timing, the need to deal with the potential for failure and memory exhaustion. Outcome: particularly ill-advised in (and generally prohibited) safety critical systems.

Generating vector embeddings for new questions/answers causes the write path to block or delays responses. The system also needs embeddings to be created reliably for later semantic search.

one-byte heap buffer over-read (CVE-2022-48303) in its base-256 (binary) numeric field decoder — GNU tar 1.34 (src/list.c, function `from_header`). Outcome: unbounded heap over-reads until an overflow check or segfault.

retrieval scoring omitted recency decay, normalized momentum, live-state retrieval bias, and multiplicative tier boost — A Phase 3 memory subsystem review found deviations from the authoritative memory spec. Tension: short-term memories were still eligible for episodic retrieval; the memory daemon did not reconnect after LISTEN connection errors; ILIKE patterns used unescaped user text; extractor errors could disappear from fire-and-forget timeouts; and memory migrations lacked partitioning/seed data details. Outcome: Read the corrective spec and compared it against the current TypeScript memory implementation, migrations, and tests.

When a GRUB script token exceeds YYLMAX size — in GRUB2 2.04 (and earlier). Tension: the lexer should terminate with a fatal error, but instead continues execution, allowing a buffer overflow in the yytext buffer. Outcome: This can lead to code execution and Secure Boot bypass.

Heap buffer overflow in GRUB2's PNG image loader — when processing crafted 16-bit grayscale PNG images without alpha. Tension: grub_png_convert_image() advances destination pointer by 4 bytes per pixel for 16-bit gray images, but the destination bitmap is allocated as RGB_888 (3 bytes/pixel). Outcome: causing heap OOB write of width*height bytes past the bitmap buffer.

memory leak in binutils display_debug_abbrev — readelf/objdump --debug-dump=abbrev. Outcome: an attacker can drive unbounded heap growth and DoS the tool.

Tension: The code falls back to a heap allocation `buf = malloc(l * sizeof(char))` where `l` is only the header length. Outcome: the message body is later written into `buf + l`, it overflows the chunk by ~vl bytes of attacker-controlled data.

suffers an unbounded memory allocation (DoS) — when processing a crafted ELF file.

Ghostscript 9.50 (psi/zfsample.c) has a use-after-free in sampled_data_finish — compacting/moving GC. Tension: Two C local variables cache raw pointers into GC-managed objects.

a very large text node (>~2GB) causes.

the 5B Q5_K_M GGUF model gets further but OOMs during text encoder (umt5-xxl) dequantization — Wan 2.2 Image-to-Video models (14B and 5B GGUF). Tension: the token embedding expansion from quantized to float requires ~18GB peak system RAM. Outcome: it still fails if system RAM is constrained.

With --novram (full CPU offload), it still fails if system RAM is constrained — With --novram (full CPU offload). Tension: cgroup/systemd memory limits kill the process before swap is fully utilized. Outcome: Adding 16GB swap doesn't help.

Tension: attacker-controlled sizes from PAX extended headers. Outcome: triggering a SIGSEGV/stack exhaustion.

Running Wan 2.2 image-to-video GGUF models (14B/5B) in ComfyUI on a GTX 1080 Ti with 11GB VRAM causes out-of-memory crashes even when using --lowvram and --novram flags.

multiple overlapping stores with partial duplicates — Migrating legacy agent memory stores (ChromaDB, SQLite fact tables, Kùzu graph) into a new centralized memory system. Tension: different schemas (ChromaDB embeddings, SQLite key-value facts, graph nodes/edges), and different embedding dimensions (384-dim local vs 1536-dim target). Outcome: Need dedup across stores and idempotent ingestion.

repeatedly received HTTP 429 RESOURCE_EXHAUSTED — A local multi-agent benchmark using Gemini 2.5 Flash. Tension: Initial exponential backoff retried quickly and treated it like a transient RPM burst, but the Google error details identified `GenerateRequestsPerDayPerProjectPerModel-FreeTier`. Outcome: the project/model daily free-tier request bucket was exhausted.

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