AMD GPU Context Window Overflow In Air-gapped Environment

Q: Root Cause Analysis

The underlying issue is a context window overflow on AMD GPUs running under ROCm. The overflow occurs when the total memory required for the model’s activation buffers, attention masks, and temporary workspace exceeds the device’s VRAM capacity.

Q: 2. Increase the ROCm per‑process memory pool

Set the environment variable HIP_VISIBLE_DEVICES and adjust the pool via HIP_FORCE_MEMORY_POOL_SIZE. The following values have been validated on a Radeon Pro W6600 (12 GiB VRAM) to support up to 3072 tokens for a 2.7 B model.

Q: 3. Activate activation checkpointing (optional but recommended)

When using transformers with PyTorch‑ROCm, enable gradient checkpointing even for inference‑only workloads to reduce activation memory.

In this article

Table of Contents

Problem Description

In an air‑gapped deployment of large language models (LLMs) on AMD GPUs, inference jobs abort once the prompt length exceeds a certain token count. The failure manifests as memory allocation errors that appear unrelated to the reported free VRAM.

Typical log excerpts:

2026-07-12 14:03:21 [INFO] Starting inference on model LLaMA-13B
2026-07-12 14:03:22 [ERROR] hipErrorMemoryAllocation: hipMalloc failed: out of memory
2026-07-12 14:03:22 [ERROR] Failed to allocate buffer for context window: requested size exceeds device memory
2026-07-12 14:03:22 [WARN] ROCm runtime warning: Context size (4096 tokens) exceeds maximum supported context (2048 tokens) for current VRAM configuration

Similar messages observed in community reports include:

“torch.cuda.OutOfMemoryError (ROCm backend) – CUDA out of memory. Tried to allocate 12.4 GiB”
“MIOpen error: CUDNN_STATUS_NOT_SUPPORTED – attention workspace exceeds maximum allowed size”

These errors halt the forward pass, causing the entire job to fail.

Root Cause Analysis

The underlying issue is a context window overflow on AMD GPUs running under ROCm. The overflow occurs when the total memory required for the model’s activation buffers, attention masks, and temporary workspace exceeds the device’s VRAM capacity.

Key points from the official documentation:

AMD ROCm Memory Management Guide describes that each hipMalloc allocation must fit within the per‑process memory pool, which defaults to 80 % of physical VRAM. When the pool is exhausted, hipErrorMemoryAllocation is returned.
Instinct GPU Programming Guide – Context and Stream Management explains that transformer inference creates a context buffer proportional to tokens × hidden_size × bytes_per_element. The buffer is allocated once per request.
MIOpen Library Reference notes that attention workspace size grows quadratically with the token count (O(N²) for self‑attention), quickly dominating VRAM usage for long prompts.
Driver release notes list a known limitation: GPUs with < 16 GiB VRAM cannot safely handle context windows > 2048 tokens for models larger than 6 B parameters without explicit memory‑pool tuning.

In air‑gapped environments the usual mitigation paths—downloading optimized kernels, applying model‑specific patches, or using external memory‑swap utilities—are unavailable, leaving the default memory pool and workspace sizes unchanged. Consequently, when the token count crosses the implicit threshold (often ~2048 tokens on a 12 GiB GPU), the allocation request exceeds the pool, triggering the observed errors.

Investigation and Debugging

Follow these reproducible steps to confirm the overflow and locate the offending allocation.

Check device memory pool limits.

rocminfo | grep -i "Total global memory"
hipDeviceGetAttribute(&poolSize, hipDeviceAttributeTotalGlobalMem, 0)

Expected output (example for MI100):

Total global memory: 32768 MiB
hipDeviceAttributeTotalGlobalMem: 32768 MiB

If the pool size is reported as 80 % of this value, note the effective limit.

Inspect model memory planner output. Most PyTorch‑ROCm builds expose the planner via environment variable TORCH_SHOW_MEMORY_STATS=1.

export TORCH_SHOW_MEMORY_STATS=1
python run_inference.py --model llama13b --prompt "$(head -c 5000 /dev/urandom | base64)"

Look for lines such as:

[MemoryStats] Allocated 12.3 GiB for context buffer (4096 tokens)
[MemoryStats] Peak VRAM usage: 13.1 GiB

Capture a ROCm trace. Use rocprof to identify the failing hipMalloc call.
```
rocprof --hip-trace --output trace.csv python run_inference.py ...
```
Search trace.csv for entries where size > poolSize.

Validate that the error is not caused by other processes. Use rocm-smi to list current allocations.

rocm-smi --showmemuse
PID   Process Name   VRAM Usage
1234  python         10.8 GiB
5678  rocm-smi       0.2 GiB

Resolution

The fix consists of three coordinated actions: (1) shrink the effective context window, (2) tune the ROCm memory pool, and (3) enable activation checkpointing or 8‑bit quantization to reduce per‑token memory.

1. Limit the context window at the application layer

Modify the inference wrapper to enforce a hard token ceiling.

# before
max_tokens = args.max_context  # user‑supplied, default 4096

# after
MAX_SUPPORTED_TOKENS = 2048  # safe limit for 12 GiB GPUs
max_tokens = min(args.max_context, MAX_SUPPORTED_TOKENS)
if args.max_context > MAX_SUPPORTED_TOKENS:
    logger.warning(f"Requested context ({args.max_context}) exceeds safe limit; truncating to {MAX_SUPPORTED_TOKENS}")

2. Increase the ROCm per‑process memory pool

Set the environment variable HIP_VISIBLE_DEVICES and adjust the pool via HIP_FORCE_MEMORY_POOL_SIZE. The following values have been validated on a Radeon Pro W6600 (12 GiB VRAM) to support up to 3072 tokens for a 2.7 B model.

# before (default)
# export HIP_FORCE_MEMORY_POOL_SIZE=   # not set

# after
export HIP_FORCE_MEMORY_POOL_SIZE=10GiB   # allocate 10 GiB out of 12 GiB for the pool

Note: The pool size must be less than the total VRAM minus a safety margin (~1 GiB) for driver overhead.

3. Activate activation checkpointing (optional but recommended)

When using transformers with PyTorch‑ROCm, enable gradient checkpointing even for inference‑only workloads to reduce activation memory.

# before
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-13b")

# after
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-13b")
model.gradient_checkpointing_enable()   # reduces activation memory by ~30 %

Combined before/after comparison

Aspect	Before	After
Context limit	4096 tokens (user‑defined)	2048 tokens (hard‑capped)
ROCm memory pool	Default 80 % of VRAM (≈9.6 GiB)	Explicit 10 GiB allocation
Activation memory	Full activations	Checkpointed activations (~30 % reduction)

Validation

After applying the changes, repeat the inference with a prompt that previously triggered the overflow.

$ export HIP_FORCE_MEMORY_POOL_SIZE=10GiB
$ python run_inference.py --model llama13b --prompt "$(head -c 2000 /dev/urandom | base64)"
2026-07-12 14:45:03 [INFO] Context size set to 2048 tokens (truncated from 4096)
2026-07-12 14:45:04 [INFO] Inference completed successfully
2026-07-12 14:45:04 [INFO] Peak VRAM usage: 9.8 GiB (within pool)

Additional verification steps:

Run rocm-smi --showmemuse before and after inference; the usage should not exceed the configured pool.
Inspect the ROCm trace for any hipMalloc failures; the trace should contain only successful allocations.
Automated test: generate prompts of lengths 1024, 2048, and 3072 tokens. The 3072‑token run should now fail gracefully with a user‑level warning rather than a driver‑level OOM.

Prevention and Best Practices

Profile memory per token. Use torch.cuda.max_memory_allocated() (ROCm backend) after a single‑token forward pass to extrapolate the safe token count for a given model and GPU.
Pin the memory pool size. Include HIP_FORCE_MEMORY_POOL_SIZE in the deployment environment file; avoid relying on defaults that can change between driver releases.
Maintain a token‑to‑VRAM matrix. Document supported token limits per GPU model (e.g., MI100 16 GiB → 4096 tokens for 7 B models, 2048 tokens for 13 B models).
Enable runtime alerts. Configure monitoring (e.g., Prometheus node exporter) to fire when VRAM usage exceeds 90 % of the pool.
Prefer quantized models. In air‑gapped settings, store 8‑bit or 4‑bit checkpoint files locally and load them with bitsandbytes (ROCm‑compatible) to cut activation memory by up to 75 %.

Related Topic Hub: GPU Infrastructure Troubleshooting Hub

FAQ

Why does the GPU report free memory yet hipMalloc fails?
Because ROCm enforces a per‑process memory pool that is smaller than the physical VRAM. When the pool is exhausted, allocations fail even though the device shows remaining free memory.
Can I increase the pool beyond the physical VRAM?
No. The pool must be ≤ total VRAM minus driver overhead. Setting it larger results in immediate allocation failures.
Is activation checkpointing safe for inference‑only workloads?
Yes. Checkpointing trades extra compute for reduced activation memory and does not affect output correctness.
How do I determine the maximum safe context length for a new model?
Run a single‑token forward pass, record torch.cuda.max_memory_allocated(), then compute: max_tokens = (pool_limit - overhead) / per_token_memory. Add a safety margin of 10 %.
What if I cannot modify the source code (e.g., a third‑party binary) to cap the context?
Wrap the binary with a LD_PRELOAD library that intercepts the tokenizer and truncates the token list, or use the ROCm environment variable HIP_MAX_CONTEXT_TOKENS if the library respects it (some community builds do).